{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第四回：文字图例尽眉目"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、Figure和Axes上的文本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib具有广泛的文本支持，包括对数学表达式的支持、对栅格和矢量输出的TrueType支持、具有任意旋转的换行分隔文本以及Unicode支持。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的命令是介绍了通过pyplot API和objected-oriented API分别创建文本的方式。\n",
    "\n",
    "| [`pyplot`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot) API | OO API                                                       | description                                                  |\n",
    "| :----------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |\n",
    "| [`text`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text) | [`text`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.text.html#matplotlib.axes.Axes.text) | 在 [`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的任意位置添加text。 |\n",
    "| [`title`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title) | [`set_title`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title) | 在 [`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)添加title |\n",
    "| [`figtext`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figtext.html#matplotlib.pyplot.figtext) | [`text`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.text) | 在[`Figure`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure)的任意位置添加text. |\n",
    "| [`suptitle`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.suptitle.html#matplotlib.pyplot.suptitle) | [`suptitle`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.suptitle) | 在 [`Figure`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure)添加title |\n",
    "| [`xlabel`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel) | [`set_xlabel`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel) | 在[`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的x-axis添加label |\n",
    "| [`ylabel`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel) | [`set_ylabel`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel) | 在[`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的y-axis添加label |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.text\n",
    "pyplot API：matplotlib.pyplot.text(x, y, s, fontdict=None, \\*\\*kwargs)  \n",
    "OO API:Axes.text(self, x, y, s, fontdict=None, \\*\\*kwargs)  \n",
    "**参数**：此方法接受以下描述的参数：  \n",
    "s:此参数是要添加的文本。  \n",
    "xy:此参数是放置文本的点(x，y)。  \n",
    "fontdict:此参数是一个可选参数，并且是一个覆盖默认文本属性的字典。如果fontdict为None，则由rcParams确定默认值。  \n",
    "**返回值**：此方法返回作为创建的文本实例的文本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fontdict主要参数具体介绍，更多参数请参考[官网说明](https://matplotlib.org/api/text_api.html?highlight=text#matplotlib.text.Text)：\n",
    "\n",
    "\n",
    "| Property                                                     | Description                                                  |\n",
    "| ------------------------------------------------------------ | :----------------------------------------------------------- |\n",
    "| [`alpha`](https://matplotlib.org/api/_as_gen/matplotlib.artist.Artist.set_alpha.html#matplotlib.artist.Artist.set_alpha) |float or None   该参数指透明度，越接近0越透明，越接近1越不透明   |                           \n",
    "| [`backgroundcolor`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_backgroundcolor) | color  [该参数指文本的背景颜色，具体matplotlib支持颜色如下](https://www.cnblogs.com/charliedaifu/p/9957822.html)                                                    |\n",
    "| [`bbox`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_bbox) | dict with properties for [`patches.FancyBboxPatch`](https://matplotlib.org/api/_as_gen/matplotlib.patches.FancyBboxPatch.html#matplotlib.patches.FancyBboxPatch) 这个是用来设置text周围的box外框 |\n",
    "| [`color`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_color) or c | color 指的是字体的颜色                                                       |\n",
    "| [`fontfamily`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontfamily) or family | {FONTNAME, 'serif', 'sans-serif', 'cursive', 'fantasy', 'monospace'} 该参数指的是字体的类型|\n",
    "| [`fontproperties`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontproperties) or font or font_properties | [`font_manager.FontProperties`](https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties) or [`str`](https://docs.python.org/3/library/stdtypes.html#str) or [`pathlib.Path`](https://docs.python.org/3/library/pathlib.html#pathlib.Path) |\n",
    "| [`fontsize`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontsize) or size | float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'} 该参数指字体大小|\n",
    "| [`fontstretch`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontstretch) or stretch | {a numeric value in range 0-1000, 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded', 'ultra-expanded'} 该参数是指从字体中选择正常、压缩或扩展的字体 |\n",
    "| [`fontstyle`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontstyle) or style | {'normal', 'italic', 'oblique'} 该参数是指字体的样式是否倾斜等                               |\n",
    "| [`fontweight`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontweight) or weight | {a numeric value in range 0-1000, 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black'} |         \n",
    "| [`horizontalalignment`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_horizontalalignment) or ha | {'center', 'right', 'left'}  该参数是指选择文本左对齐右对齐还是居中对齐                                |\n",
    "| [`label`](https://matplotlib.org/api/_as_gen/matplotlib.artist.Artist.set_label.html#matplotlib.artist.Artist.set_label) | object                                                       |\n",
    "| [`linespacing`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_linespacing) | float (multiple of font size)                                |\n",
    "| [`position`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_position) | (float, float)                                               |\n",
    "| [`rotation`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_rotation) | float or {'vertical', 'horizontal'} 该参数是指text逆时针旋转的角度，“horizontal”等于0，“vertical”等于90。我们可以根据自己设定来选择合适角度                       |\n",
    "| [`verticalalignment`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_verticalalignment) or va | {'center', 'top', 'bottom', 'baseline', 'center_baseline'}   |\n",
    "                                                    |\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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r14pERYmccYbIO++IfPSRyMUXW+evWXO837p1Vttvf2t9zssTSU+3+rpjGJZ8AwaI/OEPIosXi/zqV1abYRzv5+n1aYv2XoPXXxeJiRGpqTmxvaHB+tvt2iXy/e+L9Ohh/Y7uNHf9ugi0srLwu3Lw98vbyuLr3QUy+dElsj2n1Kvj+ppPt+bKmX/8XJZuz5Oj5TXyPx9tk6ziKn+L5TumTxfJzBSprGz++IQJIoMGidTXH2/bt08kJETkl7+0PhcUWP9iCxa0PM/8+SJKHVcerZGVJZKaaimopiQkWIrBhesm+/XXx9tycqwb/q9/LfLcc5aye+ut5uc680yR8847/vmcc0SGDj1RzoYGq+3SS088989/tn6nJUtEzj9fJCPj+M3ZhUtZPPbYie233GLdpIuLrc+eXJ/20N5rcOWVIj/4wcntEyZYcoP199+2rfl5ml6/LkJryiLQtqG6PFFhweSV1ZJV1HWN3CLCHz7aRkhQEFFhwVTXN/Lc1/t58ev9/hbNN1RVWR4w111nbY80pbIS1q2Dq66CELdQpf79ra2ZZcusz8nJMGAA3HsvPPss7N598ljZ2RAXB2FhrctUUQGXXmrN9+KLJx47eBBKSmDMmONtGzZY2zyjRh1v69EDfvEL+Otf4Sc/gaeesjx6miM11ZINoLra+p2uuMLaTmposF4icO65sHz5ief+4hcwZw5cdJG1BfXyy5CS0vw8Tee/+mrrd92ypfXr89lnrW+9uV4zZ554XnuuQV2dtcXY3BbUK6/AihXw+uuWXOedBwcOtH79ugkBFZTXHeiVGAl07ViLLUfKOFhYxR8vH8XpA6x92YtG9+SNVYf42azB3b/WeHGxtW+emdnycRFrj70pPXoc945RCpYssfbn77sPCgsthfKb31j75AA1NZYrZmvU1Fi2gn37rJt2U7k2brR+jh59vG39eujXz7qhuTN4sGUHOPNMuOOOlueMjLSUBFieQI2N8PvfW6/mcDgsReL6vX/4Q1i0CMaOhVmzWp4nPb35zy6bTEvXZ9o02L695XFdNKfs27oGn39u/e4XXnjysWHDrJ+nnw5z51rXeP58ePrpE/u5X79uglYWXiY1JpzwkCCyiqv8LUqH+XBTNiFBivNH9DjWdttZA1iwIZvXVx7ipzOb9UjuPiQmWjc+dyNy0+NKQW7uycdyc48bPsFaWbz8sqVcNm60XC9vv926ycyda/UtLm5Zlvp6K9Zi1Srradp9peBi0ybLeD5kyPG29etPXGkALF1qPU1PnWqtnDZuPLmPi6Ki46uBhATretxxB9xwQ/P9g9w2KXJzraf38eMtOZ58En7+8+bPy8uzrpH7Z4BezvCqlq5PVJRlTPeU9lyD99+HGTOs37s1EhJg0CDLcN4U9+vXTdDbUF7GFWvRlVcWS3fkM31wCglRx5f+IzLiOXNQCi9+s5/ahm6ehj0qynrqfPXV5p8Oo6Mtn/t33rGeuF0cPGh5wMyYcfI5SllP2X/+s/XZtc0ydKilELKyTj7H4bC2wj7/HBYssHz6m2PjRhgxworJAGu87dtPvAmuW2dtq9xyi+Xl06cP3H9/y9dg//7jyic6GqZPt+YZPx4mTjz55UIEbrzR2jZassRSGr/9raXQmuPtt0/8/OabEBMDI0e2fX08pT3XQAQ++KB1LygXeXmwYwcMbObhyf36dRdaMmacKi87vKH+tHiH/GvZHq+P6yuqahvkcNHJht2vdhXIna+vk4LymmbO6masWmV5Q40ZI/LyyyJLl1oG0TvvtI67vKHmzBFZuNDynhk82PKGOnLE6rNxo8jMmZYH05IllofS1VdbRnCXB9H+/Zax9D//OVmGefOsYw88cNzLyfU6fPh4v8GDRX784+OfDx2yznvySevz7t0iaWkil19ueeqIiLzwgtVn2bKT5y0utozKzz57vG3tWpHoaMu4/sYblgH93XdF7r//uOeTiBVzEBRkHRexDNPjxllxB1VuDhLu3lCPPiry6acid99ttT388PF+rV0fT2jvNfj2W6vN/fqKiHzveyKPPCLy/vvWd+Hpp0WGDBGJjxfZufPEvs1dvy4C2hvKt8pC001Yt07koousG0JEhHVzmD//+PFFi0SmTLGOxcWJXHKJFajlIi9P5IYbrJt5ZKRIYqLIWWdZSsOdyZNFbrrp5Pn79pVjnjdNXy730spK6+b8l78cP6+62nKljYiwFF3//iIzZpzoBuryZJo69eR5X31VJDxc5OjRE9u3bRO56irLIyssTKRXL8sl9qOPjl+vsDBLubmzY4flgTRv3vE2l7LYvNlSqBERlovtgw8ev5m3dX3aS05O+6/BPfc0HwA4f77I+PHWdyEyUuS000Ruu+24W7I7LV2/LkBrykJZx09dJk6cKGvcg3G8RH2jg2ClCArqOjUuHA7hxhdXce3kPswd1Yzx1tln39EKMhOjiAgN9rGE3ZR//9va08/Jad4g62vmzrX22195xb45Hn4YTNPaYgppw3Tqy+szdKhlnH/ggY6P4YvrZxNKqbVipUw6CW2zsIFFm3MY8uAi9h2t9LcoHrH6QBFf7T5KXWPLeYe+3JXPuX9ezsbDJS320XjID39oGXT/8Q9/S2K53H7xhe/zMLWGL6/Pjh2dUxSBeP28hFYWNpASG45Dup777KItuUSEBnHusPQW+4ztnQjA2kOtePBoPCM4GF54ITBWFbm5VhzHoEH+luQ4gXR92iIQr5+X0K6zNtArwYq16Grus+sPFTOudyLR4S1/LZKiwxiQEs26g3pl4VWmTGnZ28mXzJnjm3kefth6tZdAuT5t4avr5wf0ysIG0uMiCAlSHOlCqcobGh3syC1nREZcm33H901k3aFiTnV7l0ZzKqGVhQ0EByl6xEd0qW2o8poGzjotlUn9k9rsO6FvIkWVdRws7ForJ41G03H0NpRN3DStHykxbaRxCCASo8N49oZmnSBO4pyhaTx7w0RSY7vO76fRaDqHVhY2ccv0AW13CiDqGx2EBrdvoZkeF8F5wyNslkij0QQSehvKJhoaHRwpqabR0TX29X/879X8+N+r291/R24Zb646ZKNEGo0mkNDKwib+u/4IZ8xf2iWM3CLCtuwyUmLaSJPtxuItedz33mbKaurb7qzRaLo8WlnYRGZC10lVnl9eS2FlHcN7tu0J5WJC30REYMMh7UKr0ZwKaGVhE2lx1p5+fnmNnyVpm23ZZQCM6BXf7nPG9I5HKVing/M0mlOCgFMWSqk5SqmdSqk9Sql7mzl+k1KqQCm1wfm6xe3YjUqp3c7Xjb6V/ETS4yxPobyyLqAscixlMbRHbLvPiY0IZUh6LOv0ykKjOSUIKG8opVQw8HfgPCALWK2UWigi25p0fUtE7mxybhJgABMBAdY6z/XLo29MeAhRYcHkl9X6Y3qPGJOZwB1nDyQ2wrMKeMMz4lixt9AmqTQaTSDRbmWhlNkbmAaMAdKBGKAcyAc2AytEjM4WaZ4M7BGRfdac6k3gUqCpsmiO84ElIlLkPHcJMAd4o5MydQilFA9cOIxBqTH+mN4jzhycwpmDPa/q9eCFw4kK6/qZZ0UEpazswO+sOcye/ApuP3tQ9y8fq9F4QKvKQikzBvgxcAMwztl8GCgEKoFBQCrQy9l/C/Bv4HkRo6wD8vRyju8iCzi9mX6XK6XOAnYBvxSRwy2c26sDMniN607v68/p20VNfSNZxVX0T4kh2MN06knR7feeCmR+8dYG+iRFcffsIezMLeeFb/ZT2+Dg4UtG+Fs0jSZgaMtmcRC4HfgYmAHEihj9RIwJIsZZzp99gDjgHOADZ/8DHZSnubtV00CFD4B+IjIa+Ax4yYNzrY5K3aaUWqOUWlNQUNBBUdvmaEUtW46U2ja+N9hypJRz/7ycL3bke3xuZW0Df/hwG8t32XcN7WbFvkIWbMg+FpD44EXDuXJib15feahLeLJpNL6irW2o80WMNisDiRiVwDLn60GlzI6mh8wCert9zgSyT5xL3DfJnwX+6HbuzCbnftm8vPIM8AxYxY86KGubPP3lXl5beYhtj5x/bJsj0HAZt0f0ar/brIuI0GBeXnGQoCDFWaelels022l0CI98sI2M+AhudYu4v2vWYP677ghPfbabP/5gtB8l1GgCh1ZXFk0VhVLmxUqZkW0NKmKs6KA8q4HBSqn+Sqkw4Gpg4YkyKPcSbpcA253vFwOzlVKJSqlEYLazzW+kx0VQXd9IeW2DP8Vole05ZSREhdIjzvP0HcFBigEp0ezJr7BBMvt5d+1htuWUce8Fw4h0s71kJERy/ZS+vL/hCEWVdX6UUKMJHDz1hnoTaFTKfA94FfhMxPDak7mINCil7sS6yQcDL4jIVqXUI1i1YRcCdymlLgEagCLgJue5RUqp32MpHIBHXMZuf5HmdJ/NL6shzkNPI1+x/2glA1NjOrzyGZQWw8asruc+W13XyP8u3snEvolcPPrkErJ3njOIG6f17TZ2GY2ms3iqLPoDVzhfi4ACpcw3gVdFjLXeEEhEPsaykbi3PeT2/j7gvhbOfQF4wRtyeIN059N6Xlktg9LaH8PgSw4VVjFlYHKHzx+cFstHm3Oorms84ek80IkMC+adedMICVLNKsqk6DCtKDQaNzwKyhMx8kWMv4sYM7FsC/+DFdewUilzgw3ydWmOK4vADcwzLx3ZKa+tQWkxJEeHkRvAv2NL9E+JpndSy6U6GxodXP/cSv7+xR4fSqXRBCYdjuAWMXKAr4BvgKPAad4SqrvQMz6CJ68ey+R2FBTyF+cNT2dC38QOn3/BqB6sefA8+qdEe1EqexERfvf+Flbsaz2gMCQ4iMq6Bj7dlucjyTSawMVjZaGUOUwp01TK3AGsBa4CngfGe1u4rk5EaDCXju1FZmJgFpo/XFTFV7sLqKlv7PAYgerl1Rq78yt4ZcVBDhytbLPv2UPS2JRVwtGKwI/E12jsxCNloZS5BtgC3InlljpDxOgvYjwgYuywQb4uz+asUtYe9KudvUU+3ZbHD59fRVVdx5UFwKMfbeMPH7YnyD4wcMWUzBjStrvv2UPSEKFLx5JoNN7A05XFZuD7QA8RY56I8bUNMnUrHlu0nf/5ODD16KHCSmLDQ0iM6pyn1qGiKr7Y6XlQn7/4Ymc+Q3vE0jO+TS9wRmTEkRITzhc7tbLQnNp45A0lYvzILkG6K+lxEaw+EJgriwOFVfRJjur0VtKgtBg+255PXYODsJCAS2R8AuU19aw5UMzN0/u3q39QkOLW6f2JCg+onJsajc/x6D9AKTMKuAeYDiRwYoqNGhFjmhdl6xakxYWTX1Z7QrK6QOFQUZVHBY9aYnBaLI0O4WBhJYPTA9NF2MWRkmr6Jkdx9pC0dp/zkxkDbZRIo+kaePq49CxwObAU2NTkWNfznfQB6bER1DU6KKmqJzGA/PYbGh0cLqpizsgenR5rUJqVWXd3fkXAK4uhPeL4/O6ZiHgWS1paXU9uaQ1DPKj5odF0JzxVFhcA14sY79ohTHfkWKxFeU1AKQulFO/dfoZX0nAPTI1hZK84ggJs5dQUEaHRIYQEB3m8ypv3ylpKq+v5+OfTbZLOd3y1u4CPN+dw4agMpg5M9jjbsObUxNMN5iNYKTY07WTKgCTeum0KfVoJ/vIHwUGKUZnx9EnuvFyRYcF8+LPpXlml2Mm+o5WM+/0SvtrtubF6xpBUtuWUkVva9RfQk/ol8c2eQq5/fiVTHvucF7/pbBkazamAp8ribuBppczAL9QQICTHhHP6gGSiwgLLQLrhcAlvrz5MXYPD36L4jM1ZpZTXNJAaG+7xudOdxaFW7u+6lQHfXHWI4so6IkKD+fSXZ/HP68YzICWaRz7cxnpdS13TBp4qiz3Oc/YpZTY2ebUd4XQKIiIs3JgdcP+MH2/O4cEFWwjx0hbEM8v3ctbjX3hsC/Alm4+UEhEa1KHqhUPSY4kMDWZ9F605/tK3B7j3v5v597cHACtgdO6onjx/0yTSYyN4c9Xh1gfQnPJ4+rj7ChAN/IkmdSYAHeLaDEopHlqwhYtHZzCuT8fTanibA0cr6ZMURZCXlEVwUBCHiqoorqoP2AR8m7NKGd4zjpBgz917Q4KDGJUZz/rDXU9Z1GMMUDAAACAASURBVNQ38tTnuzlzUAp3zRp8wrGY8BDemTeVXgltx5xoTm08VRZjgbkixjI7hOmupMdGBFwywUNFVfT1oh2ln9P2cbCwMiCVRaND2JJdypUTe7fduQXuv2BYl6w5/v76IxRW1nH72QObNWa7kikWlNfiEDnmlKHRuOPpI9Y64OTk/5pWSYsLJ688cBZeImIpi2TvJf/re0xZVHltTG9S29DIjdP6MWtY++MrmjK2dwKnBbhrcFNEhBe+2c+wnnFMHdByKvq6BgeX/O1rHvmg66Rt0fgWT1cWvwT+q5RZDTR1oajT+aGaJz0ugj35R/0txjEKKmqpqms8doP3BpmJUSgVuMoiKiyE384Z2qkxHA7hP+uy6JUQybRBKV6SzF6q6xuZ1C+Jyf2TWnUXDgsJ4pKxGTz31X7yy2tIi9WrC82JeKosvgLCgPcAd0umwqpcF3j7DwFAely4tcR3iNdsBJ0hNSacNQ+eS2gH9u5bIiI0mCsmZNI/NTBTlR8uqiI5JqxTXmlBQYonluxiQr+kLqMsosJCePSyUe3qe8WE3vxr2T4WrM/m1rMGtH2C5pTC0/+cmUBLfoe6WHEL3Di1H1dN7EOgxKwppUiJ8dx9tC0e/8EYr4/pLX7x1gaCleLteVM7Nc64PokB59nWEocKq8gvr2FC38R2BSEOSothXJ8E3ll7mFum9w+49DQa/+JpIsEVdgniQik1B3gSqwb3cyIyv8nxXwG3YK1kCoAfi8hB57FGrMy4AIdE5BK75W0PaQFmMPxkSw578iu485zBbXf2kJr6RiJCA8sI3NDoYGt2KddO7nx40NjeCXy0OYeC8toOxWv4kue/3scbqw+z6v5ZJES1b9H/gwmZPPDeFvZ0gdQtGt/S6j6EUuaPlDLjPRlQKTNJKfPWjgijlAoG/g7MBYYD1yilhjfpth6YKCKjgXeBx92OVYvIWOcrIBQFQFFlHf9atpc9+eX+FgWAT7bk8oYNfvUvfrOfob/7hIraBq+P3Rn2FFRQU+9gdKZHX+VmGdsnAbCCGgMZEWHJtjzOHpLabkUBcMmYDL749cxuoyhEhCeW7GLJtjwcjsCNAeoKtLWyuAP4m1LmJ8CnWJXxdooYx+56SpkJwDBgEjAbmAXswko66CmTgT0iss8aW70JXAocc9EQkS/c+q8Aru/APD6lsraBxxbtIDEqjEFp/v8nzC6poVei9/3qXS6XhwqrGJ7R+Wy23mJzVikAI3t1XlmMzIgnJEixO7+c84and3o8u9ieU052aQ2/ONezasexEaHERnQ+X5i/OVxURWZiJEopCipqufXlNfRLjuJHZ/TnutP7dCjW5lSn1SsmYkzESh5YAvweWAWUKGXWKWWWKmXWA4VYdbh/D5QD3xMxOrp53Qtwf+TNcra1xM3AIrfPEUqpNUqpFUqp73VQBq/j2q4oCJDSnEdKqm0JwnLlvzpYGFjB/JuPlBIdFswAL9QJjwwLZvUD53L7zEFekMw+lu6w6obPHNp2NcCmFFXW8dNX1/Lp1lxvi+UT1h8qZtb/LWP5bssD8ZFLRvC3a8eRGB2GsXArj3683c8Sdk3atFk4A/CWKWUqYAgwBkgHYoEqLLvBFmCriFHfSXmas6g1u3ZUSl0PTARmuDX3EZFspdQAYKlSarOI7G3m3NuA2wD69OnTSZHbJiI0mNiIEAoCINai0SHkltWQkeB9O8qxWIuiwHKfvWpSbyb3T/KaJ1ogZQ9uiW/2FDKmd0KHXGDjI0NZf6iE+kYHs0cEdnLIplTXNfKrtzeSGhvOUGc6+ZDgIC4ancFFozMwP9jKS98e4PopfRnYgbQvpzLtNnCLGALscL7sIgtwD7HN5OS0IiilzgUeAGaIyLE7sIhkO3/uU0p9CYwDTlIWIvIM8AzAxIkTfbKRmRobHhDKorCylmClyLBhZREbEUpydFjArSxGZMQzIqPzW1AudueV8+clu7h79pBjtTwCjZd+PLnDWQOCgxRzR/Xg9ZWHqK5rJLILRa0/tmg7+49W8vqtpzcbiX7f3GFcNDpDK4oO4PHGnVJmT6XMu5Qyf6+UGe5sS1fK9MbdZzUwWCnVXykVBlwNLDxxfjUO+BdwiYjku7UnKqWc8qgU4AzcbB3+Ji02nPxy/6f8SIuNYMfv53Qq7UVrzJsxkBmndTxK2tsUlNfyyZYcSqs7u+g9jlKwaEtuQBu5w0KCjqXx6AhnD0mjtsHBd/sCJ5i0LZbvKuDl7w5y85n9mTaw+TiYsJAgJvS1crSt3FdITX2jL0Xs0nikLJQypwC7gUeB+wGXL+KvsdxdO4WINAB3AouB7cDbIrJVKfWIUsrl3fS/QAzwjlJqg1LKpUyGAWuUUhuBL4D5IhIwyuIf103glZtP97cYgBVc5s2APHduPWtAQNW1WLm/kHmvriOr2HtbY/1TYogIDWJbdpnXxvQmj328nWeWn7Sg9ojTByQRFRbM0h35bXcOEHJLaxjZK47fnD+kzb77j1Zy9bMreMmZhVfTNp4G5T0FvAnMwzJmu/gYeMEbAonIx87x3Nsecnt/bgvnfQu0L1TVDwRKcr2PNuWwfFcBj1420haPkLoGB1nFVfRJigoIj5NdeRUEKby67RAcpBjaI46t2aVeG9Nb1DU4eHXFQS4Z25pfSNuEhwRz7eQ+9IgPrBih1rhyUm8un5DZrsp//VOiOWNgCs9+tY8bpvbrUltt/sLT/+bRwN9EjKaO9HlA4DxOBiDrDhXz8MKtVNf5d9m7Yl8hn2zNte1G/tHmbM75v2UBY+TenVdO3+RorwcKjsiIY1tOWcDV71i5v5DKukbO7UTCRBcPXjScW6YHftoPh0P4cmc+Dod4VCL2rlmDOVpRxxurDtkonW+oqW9k1f4i9hVU2DaHp3eMYiCpmfYRWCVXNS2wv6CSf397wO+pyrNLqm0xbrvok2S5pwaKkXtXXjmDbTBCj+2dwICUaMqqAysA8YsdBYSHBLW4Z+8ptQ2NHCmp9spYdvH5jnxuenE1S7bneXTe5P5JnN4/iaeX7e2ytouGRgfGgi2MengxV/7rO95aY18RK0+VxbvAn5Qyx2K5tKYpZV6IFUW9qNUzT3ECJdbCirGwb2shkFKV1zY0cqCwypa04ldM7M2CO88kPiqwAthW7CtkQt9Er22rXPPMCn751gavjGUXz3+9j14Jkcwa6vlq6uezBlNd18iO3MDIruAJNfWN3P7aOl767iDfH5fJczdMZN5ZA22bz1ObxW+xIrPXYsVELHP+/BTLlVXTAi5lkV/mX2WRXVLN5P7NLQ69Q3J0GDHhIQGhLEKDgvjsVzOICPW/7cQXOBxC/9RoxnuxIuOUAcn8a/k+SqvqA04xAmw5UsqKfUXcf8HQDm2tTh2YzLf3ndMlo9ZfXXGQJdvzePji4dx0Rn/b5/M0kWAVcJ1S5v3ASOf5u0WMgPE6ClTSXCsLP7rP1tQ3khgddizS2g6UUvRJigqIbaigIEV/L0Rtt8Sv3t5AfaPw12vG2TaHJwQFKf5+7XivjnnO0DT+8eVevtpTwEWjM7w6tjd44ev9RIUFc9WkjgXXKqWIjQhFRCipqu8SAZcufnRGf0b1iuf0VopaeROPlIVS5jnAMhHjIHCwybE0IA3YJmI4vCdi9yAxKozQYEVZjf/2uCNCg1n2m7Ntn+cX5w4OCO+SJdvyyC2r4YdTOp9ttjkcDmH1/iJbxu4IdmT8HdcnkYSoUJbuyA84ZVHb0MjaQ8VcObE38ZGdWxnc/NIaKmobePsnnUth7wu+3JnPwNQYeidF+UxRgOc2iyXARUqZv1LKnO1qVMq8HCun00ZgnVKmLr3ahKAgxfZH5nDXLO+nBQ80Zo/owfTBnuck8jbvrDnMi980LejoPUZkxJNbVkNhgOT8uu65ldz+2lqvjhkcpDhzUApf7z4acJ5f4SHBfHH3zE5XQASY1C+JVfuL2GujN5E3yC2t4WdvrOehBVt8PrenykIBbwHXYJVXddWaeBh4GTgbqAX+4C0BuxP+jjv4cFM2P3x+JWU13otmbo6ymnq+3XPU76nKd+dXcJqNWX5dmXW35fg/OK+6rpFNWSWditpuiXkzBvKP67y7veUNXJUnvbGKvXxCL0KCFG8GsButiHD/e5upb3RgXDzC5/N35O41T8SYhKUYfqmUGQYMAJ4UMZZjRXbP8qKM3YZXVxzk0Y/8Z97Zll3Gd3sLie5EadH2sP5QCdc+t9KvEc419Y0cLKzktB72KYsRLmURAJHc6w8XU98onG6D88LIXvFM7Nd6DW9fc6SkmtMf+5xluwq8Ml5abASzhqXxn3VHqG0ITDfahRuzWbojn9+cP5R+NtriWsJTZVGD5QmF82cwVgbaII6XVc0G9DZUM2zOKuX9DSflRfQZ2SXV9IiP8ChwqSO4DOiH/BiYt7egAofAaen2JYxLiArjigmZtjoMtJfV+4tRCib0tcfT7bu9hby92j4ffk/5aFM2BeW19PXitb96ch+KKuv4bFvgpTipa3Dw+Cc7GdUrnpum9fOLDJ4+Yq4FblHKvBdrKyobq9ZFKFaG2F1YFe66TvYxH5IWF05hRS2NHkaaeovskhpbA/Jc9EqIRCn/KovDRVYgmR0xFu787xWBUXd81YFChvaI67ShtyXeX3+ERVty2p1Ow24WbsxmTGa8V5+wzxqcytPXj+fsDsRr2E1do4PZI9I5e0ia366/p8riHuBDrGR/lc7XSiAHeN9ZUW8G8I43hewupMaG4xCruIw/6jcfsTnGwkVYSBAZ8ZFk+VFZzBnZg63m+T6pB15WU09kaLBtyRnbw9WT+uCw0QA9bVAyb605zJYjpYzpnWDbPO1hX0EFW46U8eCFw7w6bnCQYs7IwNwUiQkP8Yudwh2Pvt0ixndY9SYmY2WcHQvMB04HrgUisBTFvd4Vs3uQGuMMzPNDrIWI0D8l+tg+u930Tor068oCIDo8xPansC935jP64U/ZfMS/SQUvHpPBpZ1MHtgarvQh3+z1/6bBwo3ZKIUtrrwiwl8/383rKwPH0P3Ztjy+DYDr7rGl0xmY5+6f97LzZzbWqkPTAunxEaTGhvslmaBSildv8V2K9HvnDiM02H/bFff+ZxNTBybbegOF49lst+eUeTVy2hO2ZZcRGqwYbOOWW2psOEPSY/l2T6HfS8pOH5xKeEiwLRlxlVJ8s/coeWW1XDO5t9+N+nUNDh5asIX0+Aj++9Nkv8rjsbJw5oI6HaumhDvVIoZO+dEK4/sksvqBZjOsdzvG+nGroqa+kbfWHG62Upq3yUyMJDYixK8eUX/5bBe78sr50uaAy2mDkvl0a57fbG4uJvRNPFbAyA6+Py6Te/6ziQ2HSxjnpwcAF++sPUx2aQ3zLx/td8XlafGj+4EPgJuBnwNXOl93APYnJ9F0mEWbc5j1f1/6LIPo0Ypa3l2b5Zctt30FlYjAYBs9oVwopRjWM47tfoq1EBHWHSrxyarm17OH8NU9Z/tVUXy3t5CNNlconDOqB+EhQby33r+JtB0O4fmv9jMmM57pg72TRbgzeGqRuxn4uYjRCyv47hwsG8YCTtya0rTAL95cz3Nf7fP5vPsLK9lbUGmbt0xTDhdV8et3NrI5y/d7+a4oXF/VWR7eM44dueU0Onwf4ZxVXM3RilrG2fik7SI6PIQgP3tCPbZoO8bCrbbOERcRyrnD0/lgYzb1jf7LXPTlrnz2Ha3k5ukD/L6qAM+VRQbwmfN9MRAtYgjwGPAbbwrWXdmUVcp6P9Ruzi6pJj4ylJhwewPyXPgz1mJPfgVKYWsSQXcuGt2T+y8YRoPD9zcW13dpnI+2/f61bC93vr7OJ3M1Jae0mk1ZpZw3PN32ua6YkMnEfkkUV9W13dkmKmsbGdM7gbkBUqbYU2WRjaUwAHYCFznfD8KKteg0Sqk5SqmdSqk9SqmTvKqUUuFKqbecx1cqpfq5HbvP2b5TKXW+N+TxNimx4RT4IU15bmkNPX1YIjMpOozosGC/KAsRYWRGvE/cZgEm9kvi+il9CQ/xffLEdQeLiQgNYqiNkerulNc0sGhLLuU2p4xpjs+2WcWNzh9hv7KYOSSNZ2+YSFqs/8rKXjwmgwV3nOFXl2x3PJXiA+By5/t/AA8rZe4CXgH+21lhlFLBwN+BuVjBfdcopYY36XYzUCwig4AngD86zx0OXI1VtW8O8A/neAFFWmy4Xwog+Sogz4VSit5JURz2g7L41ewhfPCzM306576CCnbk+t5ucdeswbx68+k+yzs2bVAyjQ5h5T7fZ9v9dFse/VOifba9CNZ2qj9ynG04XOLXLbDm8PQb9iDwLwAR413gUuBjrC2o270gz2Rgj4jsE5E64E3nHO5cCrzkfP8uMEtZG3qXAm+KSK2I7Af2OMcLKFJjwyko972ymNgvkWkDfZfOGKB3UpTfYy18xbxX1/L4Jzt9Pm9SdBgT+9kfaOlifJ9EwkOC+HZvoc/mBCsd+aasUmYPT/fZ/v3uvHKmP/4FH2z0bYqewoparvzXd/xpse+/T63h6QZ2uIix0fVBxPgQ+NCZTDAS6OzatBdWqnMXWVhuus32EZEGpVQpkOxsX9Hk3Gad7JVStwG3AfTp07GiKR1lQGoMA1KjqWtwEBbiu+XlI5eO9NlcLh66aDjhPq5Sl1VcxU9eWcsDFwxj2iDfeZAM7xnHSh/XttidV86S7XlcNbE3yTG+yQgQERrMpH5JPg8SCw8JZuX9s3xaK3tQWgwDU6P577osrpnsu/vE6ysPUdfg4IqJvX02Z3vw9D85XymzXzPtUwBvJFhv7pGhqYtJS33ac67VKPKMiEwUkYmpqb6tu/DDKX1ZeOeZPlUUIuKXWgS9k6J8vue7J7+CrdllPk8HPzwjjpzSGoorfWcQXbargMc/2Umjj/+2F47uyejMeJ97f0WEBpMQ5btKdkopvj8+k9UHin1W+bG+0cGrKw9y1mmpDErz3XZbe+hIPYvmbso9AG+4Y2RhueK6yMQyqjfbRykVAsQDRe0895Tku72FjDQWs+5QsU/nzSur4a+f72afDwvK7C2w/qkHpvo2hfOwnlYaFV/GW6w/VEJmYqTPFfI1k/vw+A/G+Czeoq7BwRVPf8sSp4Hbl1w2rhdK4bOYi0+25JJXVstN0+yp7tgZ2qUslDKPKGUWYT2pr1fKLHJ7lQNvAJ94QZ7VwGClVH+lVBiWwXphkz4LgRud738ALBXrsXkhcLXTW6o/MBhY5QWZvEpWcRUX//VrPt/uuy9+TmkNlXWNJPrwqQwsz5n/W7KLjVm+cxXek19BYlSoz7ZlXLiUhS8LIa07VOy3CGMR8ZntbcW+QlYfKG72KdVuMhIimTYwmYUbs32yOv9sex59k6OYeVrgZb5tr83iSSAKeAjLuOy+OdsA7McyNncKpw3iTmAxVq2MF0Rkq1LqEWCNiCwEngdeUUrtccpxtfPcrUqpt4FtTpnuEJGAq2ISFRbC5iOlHCz0neE3p9SK2val6yxYqTAADhX6JmocrIA8X3rLuEiJCefFmyYxsle8T+bLKa0mp7SG8X38k1bl7nc2svpAEV/dc47tc322PY+I0CDO9FMUs3nJSJKiw3xiWH/iyrHkltX4PfixOdqlLESMxwGUMs8D/iBieKc8VbNzycdYHlbubQ+5va8Brmjh3EeBR+2SzRskRoUSGqzI96FHVHZpDUnRYT6LO3ARERpMj7gIDhb5Zr8XYHBajN98431ZB2FfQSVhwUF+W1mM7hXPf9cd4XBRlS2lXF2ICJ9ty2P64FSff39d+Mp20NDoICQ4yKcu7p7gaYryM+xUFKcCSilSY8J9mjPJ1wF57vRJ9m2sxaOXjeLn5w722XzuHCys5Pmv9/ukLOcZg1LYbM5mlI9WMs3ND9juFbU1u4zs0hqfRG23xsp9hdz04irbvLEKK2qZOn8pn2zJtWV8b9DqykIp8w9Y20/toUbEuL/zInV/UuMiyPdhFPeM01L9FuDTLzmKb/b4xiff4RC/Lt83ZZXy+w+3cXr/JJ9sR/kjYtzFoLQY0mLD+XpPIVdNstet9Lzh6czyc/W6RhG+3FnAh5ty+MGETK+P/+bqwxSU1/rcMcMT2tqG+j5W/ER7qAa0smgHUwck+7Qo/I1+qtkL8NDFI4j00fbBu+uyePyTHXx013SfpCdviuspf8uRUluVRW1DIzc8v4pbpw/gXD89cSulmD44lc+225uyfGSveJ69YaItY3vC1AHJDEyN5pUVB72uLGrqG3nxmwOcdVqqrTVJOkurykLEaJpqQ+MF7p071GdzNTqEitoG4iJC/JK50leJC8EybpdVN5DiY08oF32To4iNCGHTkVLL68ImtmaXsXJ/ETf58SEA4IapfTl/RLrTS8j7363Sqnoq6xoCYg9fKcUPp/Tl4Q+2sSmrhNGZ3nMseG/9EY5W1DJvxgCvjWkHHY5cUsrso5QZeP5dmhPYf7SSMeanLPRxygIXhRW13PffzazcZ/9W1N78SvqnRPut3oJSilG94tlic4nVdQeteJnxPkhL3hpjeicwe0QP2wIg31ufxbT5S/2SX6w5vj8hk6iwYF7+7qDXxnQ4hGeX72N0ZjxTB/g2HY+neFr8KEgp8w/O2Ir9QI4zBuNWe8TrnnyyJZeJf1hCVrH9/wS5pZYh3R/bMgBhIUG8seoQ6w7ZH2uxt6CCgWn+3fMd1Sue3XkVttqI1h8qoVdCpN/+pu7syS/ntZXeu3m6s2R7HgNTo231tvKEuIhQfnbOYCZ7MRdXUJDiiavG8tBFwwOiZkVrePpIcC/wS+AprMR9lwGvA08qZV7nZdm6LeEhQRytqCPPB0bubGeMRUa8f5bysRGhpMSE2Z4uoa7BwaGiKgak+DdFwk9nDmT9Q+fZmlbaCsbzX9ladxZvzeOB97aQX+Zd776SqjpW7ivivOGBUcvBxU9nDuTKSd7N2TSmd4JPk0F2FE+/0fOAX4sYD4gYH4oYC0WM32Apkd95X7zuSWqstade4AP32ZwS58oi3j/7+AB9k6M5YLOyqGlo5EfT+jFtkH+X8glR9saz1NQ3MiIjjrMG+zanWUuc4/RS+nKndz3qF2/NpcEhXDAqsJQFQEVtA2+vOdzp3FhrDhTxm3c2UuiHkgUdwVNlkQ5820z7MqBfp6U5RXBtH/hiZZFbVk1KTLhf3Sz7JkfZHrEeFxHKgxcNZ9pA/9cq/tvS3Tz/9X5bxo4IDea5Gyd5/em2owztEUuPuAi+2Jnv1XE/3JRDn6Qov8WRtMbyXQXc8+4mPtzUcTugiPDHT3bwxc58osJ85wTSGTxVFllAc35sYwDfJrjvwiRHhxEcpHwSmHfe8HTuPHug7fO0xsDUGEKDg2zdxy+sqPVp+urW+G5fIe+tz7Jl7LqGwCqIo5Ti7KGpfLX7qFf/vk9cNZYnrx4bkPv4c0b0YFjPOP706c4O/z0Wbcll9YFi7p49hMiwgKvR1iyeKosngSeUMu9RyjxDKXOaUuavnO0vtXGuxklQkOKKCZmc5gOf6nOGpnPTGf1tn6c17jh7EMvvOdvWffyHP9jG+X9Zbtv4njCqVwI7c8ttiaW55tkV3PXGeq+P2xlmDkmjrtHB7jzvZRdOiQn3WyqTtggKUtw7dyiHi6p5vQPG/dqGRh5btJ2hPWK5MsBqVrSGp+k+ngL+jGWfWA58jVXW9B3A8Lp03Zj5l4/m0rHN1mbyKrvyyqn0Q1lIX7M7r9wvCQSbY3RmPPWNws7ccq+OW9vQyOasUr+lbmmJmUNS2fjQbIZnxHllvMc+3s6izTleGcsuzhqcwrSByTy1dI/H9chf/vYgh4uqeeDCYX5z8+4IHj/qiRgPAynAKGA8kCxi3CZi+L6CexenweYUHOU19cx+YjmvrLDHtbG91DY08qMXV/HuWnu2Zhodwr6jlQFTLMa1z77Zy/EWW46UUdfoCBhPKBfhIcFe20rJL6/h2a/2sd3LitbbKKX47Zyh9E+JpsjDgleXjM3gdxcNZ3qAOCm0F0/jLP6jlHk9ECxibBMxNogYvq9S3w34/YfbmPLY57bO4Yqx8PeTaHhIMOsPl7DepuJLh4uqqGtwBIyyyEyMZEBKNNV13t2Gcl2/8QG4PbMnv4KL/voVqzpZWnbR5lwcAheN7uklyexjTO8E3p03lb7J7YvtaXQINfWNpMdFcPOZ/t0a7ggdqZT3HFZ51deVMucqZXYN60yAERsRwtGKOluNvtlOZREI6RL6Jkfb5hG1O9/aKw8UZaGUYumvZ3LLdO+mb1i5v4g+SVGkBUAwXlMyEiLYV1DZacP+h5uyOS09xif2PG+glKK0up573m3bBXb+ou1c/cwKrz9E+ApPbRbfx3Kf/RlWGdX3gWylzL8qZU6yQb5ui6vmwlEbfaxznQF5PQLg5tIvOcq2WIvT0mN48MJhXeYG01EuHpPBHX72bGuJqLAQ5ozowYebcjrslbavoILVB4q5ZEyGl6WzlwNHK1mwIZtbX17T4u/+xqpDPPvVfsb2Tugy3k9N6YjNolTEeFHEuACr9vaDwOVYxm5NO0lzBubZGWuRXVKDUtAjAAyifZOjyS6ptsVDqG9yNLdMH+DTpIVtseVIKbOfWObVuueXjMmwPR14Z7hsfC/KaxpYuqNjMRfFVfWMyYwPmBiS9jKmdwJ/uWos6w+XcNcb68kuOV4ZsqymnqeX7eV3729h5pBUHrxwmB8l7RydSSQ4BcsD6mEspfFdZwRRSiUppZYopXY7f560MauUGquU+k4ptVUptUkpdZXbsX8rpfYrpTY4X2M7I4/duALzvJ0mwZ3zR/Tgj5ePttVltb0M7xnHxH5JlFV73zNr9YGiY/aZQKFnfAS78ipY4aUEijtzyzlw1HcVBzvCtIEppMWG8991Rzp0/oS+iSy480y/VTrsDHNH9eTBC4fz6bY8ps1feixx5yMfbGP+oh1MCn8YagAAGz1JREFUHZjMX68ZZ1vSRV+gPClCrpQ5BrgWuBLoC+zFyg31qoixu1OCKPU4UCQi85VS9wKJIvLbJn1OA0REdiulMoC1wDARKVFK/Rv4UEQ8qgU+ceJEWbNmTWdE7xDFlXU8tXQ3l4/P9Fnd5u6IiDDSWMwVE3vz8CUj/C3OCZz752VkJkby7x9N7vRYt7y0mr0FlXzx65mdF8xG3lh1iIjQIC4b51nNh63ZpfROiiIuItQmyXzDwUJrS+qqSb1Jj4tgX0EFVXWNXeZ/XCm1VkSaLSDi6bp9NVACvAW8JmKs6KxwblwKzHS+fwn4EjhBWYjILrf32UqpfCDVKVOXIjE6DONie29u3+49Sp+kKDITAyNrpx3klNZQWdcYMMZtd07vn8SCDdnHait3lEaHsHJ/UZfwELpmsufbZA6HcOfr6+kZH8Hrt06xQSrf0Tc5mrtmHS/rOyBAYn+8gaff4POAniLGz7ysKADSRSQHwPmz1VoZSqnJQBjW6sbFo87tqSeUUv7LnNdOauobKfbQR7u9OBzCTS+u9mru/c5yxdPf8tjH27065p4A84Ry5/QByVTUNrAtp3Pe5duyyyivaWBKgNc7cFFR28Azy/e2OxXGst0F7D9ayVVdzFZxquGpN9Qy4FdKmTEASpm/db1vD0qpz5RSW5p5XeqJHEqpnsArwI9ExPWNvA8YCkwCkmiyKmly/m1KqTVKqTUFBd7NlukJl//zW+5+Z6MtYxdW1lHX4KBXALjNuqhrcLA127thOYHmNuvOlP5JXDauF2Ehndundtk9uoqyWHOgiP/5eEe761y8+M0B0mLDmTsy8FdOpzId+RabgMu37fdu79tERM4VkZHNvBYAeU4l4FIGzbpUKKXigI+AB0VkhdvYOWJRC7wItLhRLCLPiMhEEZmYmuq/KMq02HDbkgm6PDICIcbCxaC0WHbleTcyd09+BYlRoSRHh3l1XG+QFhfBE1eNZWiPzqXB+G5fIQNSogOi2FF7mHFaKmcOSuGpz3dTWt16YodPt+ayfFcBN53Rr9NKVWMvHfnrqBbed5aFwI3O9zcCC06aWKkw4D3gZRF5p8kxl6JRwPeALV6UzRbSYiNsc5094lQWgbSyGJweQ355LaVV3ssMM2/GAP5+7fiAzE4KlgH+YGFlp2ofPP6D0Tx59TgvSmUvSinuu2AoJdX1/OPLPa32/XZvIcN7xnHLmYFdf1rTCddZG5gPnKeU2o1lG5kPoJSaqJR6ztnnSuAs4KZmXGRfU0ptBjZj5a76g2/F95y0uHAKK2o7XUSlObIDUFmclm5tFe3O997qom9yNNMG+b+GRUt8uCmHGf/7ZaeSCqbEhDMqs2t407gYkRHPZeN68eI3B1otH/zwJSN46ydT9KqiCxAwUUwiUgjMaqZ9DXCL8/2rwKstnH+OrQLaQFpsOA6xajF4O4XDhaN70i85mrjIgPkTM6xnHJeMyfBaJbniyjo+3pLDucPSA3aLZnxfK1xo5f7CDmVlXbQ5h6ziam4+sz9BXShDKcCvZw+hpKqe+saTH4a+2JFP76RIBqXFEtvF3WVPFQLnTnIKMql/Eg9cMMyWKnY94yPp6ae62y3RMz6Sp67x3nbKhqwSHnhvC4NSYwJWWfRKiCQzMZJV+4v4UQfqivxr+T4aHcKtZ3W9bZqMhEheuMnKAlRWU8/S7fnERYbw2opDLN2Zz7SBybx2S9d2lT2V0MrCjwztEddp42dLLNqcQ6/ESEZnBlY6axGhvLbBK8FX25yeVcO8VEfBLk7vn8yXO/MREY9sK0dKqtlwuIR75gyxUTrf8Mp3B/nfxTsBqwb9nWcP4sd+Lsql8Yw2lYVSZl8Rw90H7mJgn/P9Ba73Splnihg6P5QHiAiHiqoICwny+irggfe3MGdkj4BTFr9bsIVPt+ax6oFzOz3WtpwyeidFBnzU7+kDkvjPuiy2Zpd5FMnrKgDUHVxK580YSM/4CCJCgzlveHpApKDReEZ7/mI7lTKfVcpMBRAxPhMxGpzvlwCnKWUuxoq41njI7CeW8/xX+706ZnVdI0WVdQFl3HaRmRjlNY+o7dllDO8Z2KsKgNnD03ny6rEeV/JbtCWXYT3j6J/SvnoJgUxwkOL74zO5YFRPrSi6KO35q90MnA/sUsr8hat+hVJmolLmU8AG4DTgevvE7J4opchIiCTHy0nwAtFt1oXLI2pXJz2iqusaOVBYyfCege8llBAVxqVje3mUmrquwUFosOoSKT40pwZtbkOJGK8pZf4XuAcrCO82pcx3gDuwlM39wFMihj15K7o5GQkRZJdWt93RAwIxIM/F4DSr5sTuvAom9Uvq8DiRYcGsf2g2niTC9CcVtQ288t1Bpg5MZmzvtrcGw0KCePO2qV3m99N0f9pl4BYxqgFTKfNtrLoVDwLrgfNEDHtqZZ4i9IyP5OvdR7065rEYi8TAUxa9/r+9Ow+vqj4TOP59s5AQYhKyCAkhhB0qIEuKgyCPUtECKnS1Oh2casd5ZrSP2umidkGsdmz7TO20tn3Gpa21datLxaW1VAHFsgjIJpRCwhYSIWwJScj+zh/nBC7hJvcmucu5ue/nec5z7j3nd859j8H73nN+W1Z/0volhqQnd2Z/b9dV+EoU4ZG3d7PvaF1QyaK2sYX0lCTPdjY08Seoh4ciSxNElv4HsApIBl4AJgGvuvNamB4qyOrPkVMNIZ1e9dqLC3jtK7M8MUNeRwkJwteuGsuccV2OExnQU2v389g7ZYELekT/folcPWEwb2wPPJPckZoGpt6/nBc39m6KUmNCKWCyEFk6F9gKPAK8AYxRXXI9MBloAN4TWfq8yFJrB9cD8yYM5qc3TCGUTxsGpCQxYUgmiR7txHXzrOHMHtO7Mble2FjOil09m5EtWhZNdmaSWxkg7j9uPkRTaxuTYqzXtunbgrmzeA2oBi5RXfKvqks+AlBdskN1yZU4Q3BMx0koppvG52dwzaSCkA538Nz7B/jrjsMhO1+oNTS3sv1QdY8nrm9pbePvlbHREsrXpSNzyE1P4Y8fVHRa5mR9Ez9fUcrMUTmM7uNzipvYEsw31CLVJTNVl/idTk51yYs4Q4PfG9LI4kRTSxtrSo9x8Hjn4+d0189XlPLq1s6/kKJtTekxrvnZarZXVPfo+H3H6mhsaevR8BnRlJSYwKemFNCq2mnF9f++tZtTDc18e8HHIhydMV0LmCxUl/wpiDINqkt+FpqQ4ktzaxs3PLaW17ZWhuR8bW1KZfVpT7aEaje6fUDBw7U9Or59ToxYSxYA984fz2OLS/xWXNc0NPPChnKu//hQxsfYXZPp+2y4jygbkJJERmoSlSFqPltV20hzq3o6WRRk9q5F1Mn6ZgamJXe7k5sXtCeJ0qpaVu2q4uZZZ6v6MlKTefOu2aTYCKzGgyxZeEBBVn8qToamY97ZDnneawnVLiFBuKgggy3lPZs6/aZLi1k8Y1hMNyt9et0Bnli9l+wB/UhPSeK90qPcNXeMp5O8iW+WLDwgPzP1TN+I3jo7j0VaSM4XLtOGZfPE6jIamlt7NGR5LCcKgG98cizr9x7nzuc2A5CSlMCCifmU9KKjojHhZMnCAwqy+rP5YM9+ZXe0YGI+l34nl4xUb/9pP1dSyOzRud1u3rv3aB23P72J+xdOYJo7V0QsSklK5NHF03hp0yGmFGUxtWhgyOb5MCYcvP2NEie+NLOYT08dEpJziQjZHpyPuqOReek9qnP4W+lRPqyoYWBa7PTe7kx+Zn9uu2JUtMMwJiiWLDxg1IWha0//y5WlZA9I5vqPF4XsnOHy/r7jHDhWz2emFQZ9zJrSYwzKSOkTI7EaE0s80+xCRLJFZLmI7HbXfp8xiEirz/zby3y2DxeRde7xz4mI939eu6pPN/PSpvKQ9LV4ev1+3ttzLARRhd9Lm8pZ+uqHtAU5B7mqsrbsODNG5MR8nYUxscYzyQK4G3hLVUcDb7nv/TmtqpPd5Tqf7T8AHnaPP4EztHpMqK5v5qvPb2FNWe++5Jta2jh04jTFOd6u3G43bVg2NQ0t7D4SXH+LPUdqOVrbyIyROWGOzBjTkZeSxULgSff1k8CiYA8U52fmHJwBDrt9fLQNykwBoLKXzWcPnqinTWFYTmw8oilxK6g37D8eVPlWVeZNGMylI3PDGZYxxg8vJYtBqloJ4K47G5Y0VUQ2iMhaEWlPCDnASVVtcd+XA6GpMY6AlKREctNTet0xb/+xOgCKc2PjzmJYThq56f3YuC+4Ue7HDc7gl1+cxtDs2Lg+Y/qSiFZwi8hfgcF+dn2rG6cpUtUKERkBvC0i24AaP+U6fRAuIrcCtwIUFXmjIrggK/VMh7qeOlHXTEpSQszcWYgI04YNZOdHgXtyt7Uph081hHyucmNMcCKaLFT1ys72ichhEclX1UoRyQf8juOsqhXuukxEVgJTgBeBLBFJcu8uCoFOR9JT1UeBRwFKSko8MRVZQWZ/9lT1bKykdp+ZVsinpgwhlup+f/CZSVyQGrgZ7M6Paljw09X84p+nMn+iTTVqTKR56THUMuAm9/VNwCsdC4jIQBFJcV/nAjOBHeoM4bkC+GxXx3vZtxaM56lbpvf6PAkJElMthbLS+gXVMW9NqVP5P6Uo8CxzxpjQ81KyeAiYKyK7gbnue0SkREQed8uMBzaIyBac5PCQqu5w930T+KqI7MGpw3giotH30tDstF4/Yrnj2Q/43dr9IYooch58fQe/WLmnyzJrSo9RnNP7/0bGmJ7xTKc8VT0GfMLP9g3Al93XfwMmdnJ8Gc4kTDHpcE0DL2ws59pJBRT1oOlrS2sbr2+tpNCD824HcrimkWfXH2TxjGLSU87/J1nb2MK6vce59mJ7/GRMtHjpziKuVZ9u5kdv7uKDg8G1DOqo4mQDLW0aM5Xbvm6eNZxTjS38YcNBv/ufXX+A2saWmOiVbkxf5Zk7i3g3LCeNBIHSqroeHb+vvdlsDCaLyUOzmFqUxa/f28fiGcXn1WHceEkR+Zn9mTzU6iuMiRa7s/CIlKRECgemUdrDFlFn+ljESO/tjm6ZNYIDx+t5a+f5c4en9UtiwSR7BGVMNFmy8JAReQMo6+GdRWJCAuMGX0DeBSkhjioyrr5oEItnDDvnMVpDcyvX/98aVuzy24raGBNBliw8ZERuOodO1OO0BO6eGy8p4s93zo6pZrO+khITuH/hBMYMSqesqpaT9U38YcNB1u09TnKC/TM1JtqszsJD/uuqMdw7f1zMfuGHQl1TK3P+Z9WZ91OKspg5ygYONCbaLFl4yAA/zUaD0dqmXPXwKv7tshF8YXpstxhKShAevv5iTtQ1U326mQWT8uM6eRrjFZYsPKShuZUHXt/BZaPzuPoif0No+VdZfZrSqrrOB8OKIanJiXxqSvCTIRljIsMeBntISlICf/yggvf2HO3WcfuPOZMmDYvRllDGGO+zZOEhItKjFlGx3MfCGBMbLFl4zIjcAZR1s6/F7sO19E9OZHBGapiiMsbEO0sWHjMyL52K6gbqm1oCF3YVZafx6alDSAhi9FZjjOkJq+D2mFEXpjMkqz9VpxoZlhPcn+fmWcPDHJUxJt5ZsvCYeRPzmdeNyX0aW1pJECE50W4SjTHhY98wMe61LZVctORNDh6vj3Yoxpg+zJKFB93z0lb++42dQZXddqiapAShICv25rEwxsQOewzlQeUnTnOyviaostsOVXNRQUZQU5MaY0xP2Z2FB43McwbTCzSgYGubsqOihglDMiMUmTEmXnkmWYhItogsF5Hd7nqgnzJXiMhmn6VBRBa5+34jInt99k2O/FWExsgL06lraqX8xOkuy5VW1XK6uZWJliyMMWHmmWQB3A28paqjgbfc9+dQ1RWqOllVJwNzgHrgLz5Fvt6+X1U3RyTqMJhenA3AmrJjXZbLSE3m61ePZfrw7EiEZYyJY15KFguBJ93XTwKLApT/LPAnVe1zzYDGDErnstG5pCYndllucGYqt10xisKBNiaUMSa8vJQsBqlqJYC7vjBA+S8Az3TY9qCIbBWRh0UkNqeMwxkj6qlbLuG6iwu6LLfpwAlO1DVFKCpjTDyLaLIQkb+KyHY/y8JunicfmAi86bP5HmAc8HEgG/hmF8ffKiIbRGRDVVVVD64kMhpbWqlr9D/sR0trGzc+tpafvb0nwlEZY+JRRJOFql6pqhP8LK8Ah90k0J4Mupp4+fPAy6ra7HPuSnU0Ar8GpncRx6OqWqKqJXl5eaG5uBA7UdfE5KXLeWb9Ab/7S6vqaGhuY2JhRoQjM8bEIy89hloG3OS+vgl4pYuyN9DhEZRPohGc+o7tYYgxYgYO6Ed+Zmqnc1tsLT8JYC2hjDER4aVk8RAwV0R2A3Pd94hIiYg83l5IRIqBocCqDsf/XkS2AduAXOCBCMQcVjNH5bJu73GaWtrO27dsSwWDMlIYnpsehciMMfHGMz24VfUY8Ak/2zcAX/Z5vw8Y4qfcnHDGFw0zR+Xy1Nr9bD548pzmseUn6nl391G+dtUY67ltjIkIzyQLc74ZI3JIEHhvz9FzkkXhwDSW3zWbvAtitsGXMSbGWLLwsMy0ZB5YNJEpRVnn7Rs96IIoRGSMiVdeqrMwftx4SRHj8zM4WtsIwOPvlnHb05v81mMYY0y42J1FDDhS08DVP3mHeRPzWfn3IxTnDqBfkuV5Y0zk2DdODMhK68fnS4by9LoDVFQ3cPNMm0bVGBNZdmcRA/olJXDP/PFcNjqPdXuPccW4QCOhGGNMaFmyiCGzRucya3RutMMwxsQhewxljDEmIEsWxhhjArJkYYwxJiBLFsYYYwKyZGGMMSYgSxbGGGMCsmRhjDEmIEsWxhhjAhJVjXYMUSUiVcD+Hh6eC/ifyq5viZfrBLvWvsquNTjDVNXvXNNxnyx6Q0Q2qGpJtOMIt3i5TrBr7avsWnvPHkMZY4wJyJKFMcaYgCxZ9M6j0Q4gQuLlOsGuta+ya+0lq7MwxhgTkN1ZGGOMCciSRQ+IyCdFZJeI7BGRu6MdT7iIyK9E5IiIbI92LOEmIkNFZIWI7BSRD0XkjmjHFC4ikioi60Vki3utS6MdU7iJSKKIfCAir0U7lnASkX0isk1ENovIhpCe2x5DdY+IJAL/AOYC5cD7wA2quiOqgYWBiMwGaoHfquqEaMcTTiKSD+Sr6iYRuQDYCCzqo39XAQaoaq2IJAOrgTtUdW2UQwsbEfkqUAJkqOo10Y4nXERkH1CiqiHvU2J3Ft03HdijqmWq2gQ8CyyMckxhoarvAMejHUckqGqlqm5yX58CdgJDohtVeKij1n2b7C599lejiBQCC4DHox1LLLNk0X1DgIM+78vpo18q8UpEioEpwLroRhI+7mOZzcARYLmq9tlrBX4CfANoi3YgEaDAX0Rko4jcGsoTW7LoPvGzrc/+Kos3IpIOvAjcqao10Y4nXFS1VVUnA4XAdBHpk48ZReQa4Iiqbox2LBEyU1WnAvOA29xHySFhyaL7yoGhPu8LgYooxWJCyH1+/yLwe1V9KdrxRIKqngRWAp+McijhMhO4zn2W/ywwR0R+F92QwkdVK9z1EeBlnMfmIWHJovveB0aLyHAR6Qd8AVgW5ZhML7mVvk8AO1X1x9GOJ5xEJE9EstzX/YErgb9HN6rwUNV7VLVQVYtx/l99W1W/GOWwwkJEBriNMxCRAcBVQMhaMlqy6CZVbQFuB97EqQR9XlU/jG5U4SEizwBrgLEiUi4it0Q7pjCaCfwLzi/Pze4yP9pBhUk+sEJEtuL8+Fmuqn26SWmcGASsFpEtwHrgdVX9c6hObk1njTHGBGR3FsYYYwKyZGGMMSYgSxbGGGMCsmRhjDEmIEsWxsQJEcYFWW60iN/OpyaOWbIwfZoIw0UoE0Hd5aQIv/XZ/1sRTrtlhnfz3HeKcJ8Il3fYPlGEChFe9cKXrggZIvwGzl53ANcD74vwsfBFZWKNNZ01fZ4IqcBunN72R4GRqtS4+0YAfwEmqNLQzfPuA4YBS1W5z2f714AfuW/zVAn5CKDBEiEDWAXkApNUORHkcX/AGVn5ClU+CGOIJkbYnYXp89wksMR9mwvc47P7+8CD3U0UATyPM4TGj6OZKFw/ByYDDwWbKFzfBjKB50XoF5bITEyxOwsTF0RIBLYAFwENwBggD3gSuFi1eyOSivAucAnO8N5VOKO3Pg2sxRk2pNgtOhxn7LBN7mcD/BKnt+0V7rH/DjThfEHPBv4GLFblI5/P+xJwF5ABtOLMQfENVQ53EWMxUIYz+GWxKvvd7f2BH+AMrd8C1AE1wGOqPOlz/F73Ohar8lR3/vuYvsfuLExcUKUVuNd9mwo8APwQuLc9Ubj1Dxpgudw932WcHUDyF6pMUOX7qrwNfKnDZzep4juq6xjgc8B33dfPACWqzAf+jPP45772wiL8J/ArnC/9kcD3gMXAm24S7Mw895jG9kTh+i7wFWCVKiNVmQS8g5O8fP3DXS/o4jNMnEiKdgDGRIoqy0RYDczC+bJdrcqrPkWOArsCnKY+BKG8okqbyJkv48HACvd1+zbf0UK/5a7Xq9IqcmaejYtxBov7Uyef0976qeMEVpPc9VSRM3cyP4bzKrTbjwuqFZXp2yxZmHjzI5xkAfCK7w5VHgEeiUAMp9x1UxfbBgKIcCFQ4G67ToTNQCKcefzU1cRbA9x1c4ftq4D5OI/FVgEngeeA73Qo135cehefYeKEJQsTbzqd0EiE23FGFO7KYlXWhzYk/+G4a9+K92WqdGfk3/YElOy7UZUfinAY53HZpUAWTr3JODinGXB7xfYpTNyzOgtjzsoFxgZY0nzKn1MpLkKxSGh/gLlNfNun8fWddAsRXhOhqxnudrrrHN/+HiLcAixX5XKcSv72O6ySDsdnu+sdPQjd9DGWLIxxqXKfKhJgWelzyCF3nSNCGs5EM+FoZvqgu54twngAET6Lkzy6+iJ/Ayeh9YNzOhxeC9wtQoIq1ZxNKu93OH6su7a5Lgyoqi22xMUC+gDoXlB1l0rQl3txvjmgO0DrQbeD3uNu8/2MXaBj3f3t28pBF/op92nQI+77JtB3fT7rdvez6kDXgz4DOjSIGB93z3eXz7Y7QDeC/sNdykF/B1roU2aCe9xO0KRo/+1sif5i/SyM6cNESMdpaTUUpz9Jp/0yfI5JwJkqeAZwuSrbwhuliQX2GMqYPkyVWpxK65dxpgIOxv04ld7/ZInCtLM7C2PihAhDVc9Ulve6nIkvliyMMcYEZI+hjDHGBGTJwhhjTECWLIwxxgRkycIYY0xAliyMMcYEZMnCGGNMQP8PrW4QGBpx8d0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fontdict学习的案例\n",
    "#学习的过程中请尝试更换不同的fontdict字典的内容，以便于更好的掌握\n",
    "#---------设置字体样式，分别是字体，颜色，宽度，大小\n",
    "font1 = {'family': 'SimSun',#华文楷体\n",
    "         'alpha':0.7,#透明度\n",
    "        'color':  'purple',\n",
    "        'weight': 'normal',\n",
    "        'size': 16,\n",
    "        }\n",
    "font2 = {'family': 'Times New Roman',\n",
    "        'color':  'red',\n",
    "        'weight': 'normal',\n",
    "        'size': 16,\n",
    "        }\n",
    "font3 = {'family': 'serif',\n",
    "        'color':  'blue',\n",
    "        'weight': 'bold',\n",
    "        'size': 14,\n",
    "        }\n",
    "font4 = {'family': 'Calibri',\n",
    "        'color':  'navy',\n",
    "        'weight': 'normal',\n",
    "        'size': 17,\n",
    "        }\n",
    "#-----------四种不同字体显示风格-----\n",
    " \n",
    "#-------建立函数----------\n",
    "x = np.linspace(0.0, 5.0, 100)\n",
    "y = np.cos(2*np.pi*x) * np.exp(-x/3)\n",
    "#-------绘制图像，添加标注----------\n",
    "plt.plot(x, y, '--')\n",
    "plt.title('震荡曲线', fontdict=font1)\n",
    "#------添加文本在指定的坐标处------------\n",
    "plt.text(2, 0.65, r'$\\cos(2 \\pi x) \\exp(-x/3)$', fontdict=font2)\n",
    "#---------设置坐标标签\n",
    "plt.xlabel('Y=time (s)', fontdict=font3)\n",
    "plt.ylabel('X=voltage(mv)', fontdict=font4)\n",
    " \n",
    "# 调整图像边距\n",
    "plt.subplots_adjust(left=0.15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.title和set_title\n",
    "pyplot API：matplotlib.pyplot.title(label, fontdict=None, loc=None, pad=None, \\*, y=None, \\*\\*kwargs)  \n",
    "OO API:Axes.set_title(self, label, fontdict=None, loc=None, pad=None, \\*, y=None, \\*\\*kwargs)  \n",
    "该命令是用来设置axes的标题。  \n",
    "**参数**：此方法接受以下描述的参数:  \n",
    "label：str，此参数是要添加的文本  \n",
    "fontdict：dict，此参数是控制title文本的外观，默认fontdict如下：\n",
    "```python\n",
    "{'fontsize': rcParams['axes.titlesize'],\n",
    " 'fontweight': rcParams['axes.titleweight'],\n",
    " 'color': rcParams['axes.titlecolor'],\n",
    " 'verticalalignment': 'baseline',\n",
    " 'horizontalalignment': loc}\n",
    " ```\n",
    "loc:str，{'center', 'left', 'right'}默认为center  \n",
    "pad:float,该参数是指标题偏离图表顶部的距离，默认为6。  \n",
    "y:float，该参数是title所在axes垂向的位置。默认值为1，即title位于axes的顶部。  \n",
    "kwargs：该参数是指可以设置的一些奇特文本的属性。  \n",
    "**返回值**：此方法返回作为创建的title实例的文本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.figtext和text\n",
    "pyplot API：matplotlib.pyplot.figtext(x, y, s, fontdict=None, \\*\\*kwargs)  \n",
    "OO API:text(self, x, y, s, fontdict=None,\\*\\*kwargs)  \n",
    "**参数**：此方法接受以下描述的参数:    \n",
    "x,y：float，此参数是指在figure中放置文本的位置。一般取值是在\\[0,1\\]范围内。使用transform关键字可以更改坐标系。  \n",
    "s:str,此参数是指文本  \n",
    "fontdict:dict,此参数是一个可选参数，并且是一个覆盖默认文本属性的字典。如果fontdict为None，则由rcParams确定默认值。  \n",
    "**返回值**：此方法返回作为创建的文本实例的文本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.suptitle\n",
    "pyplot API：matplotlib.pyplot.suptitle(t, \\*\\*kwargs)  \n",
    "OO API:suptitle(self, t, \\*\\*kwargs)  \n",
    "**参数**：此方法接受以下描述的参数:      \n",
    "t: str,标题的文本  \n",
    "x：float,默认值是0.5.该参数是指文本在figure坐标系下的x坐标  \n",
    "y：float,默认值是0.95.该参数是指文本在figure坐标系下的y坐标  \n",
    "horizontalalignment, ha:该参数是指选择文本水平对齐方式，有三种选择{'center', 'left', right'}，默认值是 'center'  \n",
    "verticalalignment, va：该参数是指选择文本垂直对齐方式，有四种选择{'top', 'center', 'bottom', 'baseline'}，默认值是 'top'  \n",
    "fontsize, size：该参数是指文本的大小，默认值是依据rcParams的设置：rcParams[\"figure.titlesize\"] (default: 'large')  \n",
    "fontweight, weight：该参数是用来设置字重。默认值是依据rcParams的设置：rcParams[\"figure.titleweight\"] (default: 'normal')  \n",
    "[fontproperties](https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties):None or dict,该参数是可选参数，如果该参数被指定，字体的大小将从该参数的默认值中提取。  \n",
    "**返回值**：此方法返回作为创建的title实例的文本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.xlabel和ylabel\n",
    "\n",
    "pyplot API：matplotlib.pyplot.xlabel(xlabel, fontdict=None, labelpad=None, \\*, loc=None, \\*\\*kwargs)  \n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;matplotlib.pyplot.ylabel(ylabel, fontdict=None, labelpad=None,\\*, loc=None, \\*\\*kwargs)  \n",
    "OO API: &emsp;Axes.set_xlabel(self, xlabel, fontdict=None, labelpad=None, \\*, loc=None, \\*\\*kwargs)  \n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;Axes.set_ylabel(self, ylabel, fontdict=None, labelpad=None,\\*, loc=None, \\*\\*kwargs)  \n",
    "**参数**：此方法接受以下描述的参数:        \n",
    "xlabel或者ylabel：label的文本  \n",
    "labelpad:设置label距离轴(axis)的距离  \n",
    "loc:{'left', 'center', 'right'},默认为center  \n",
    "\\*\\*kwargs:[文本](https://matplotlib.org/api/text_api.html#matplotlib.text.Text)属性   \n",
    "**返回值**：此方法返回作为创建的xlabel和ylabel实例的文本。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#文本属性的输入一种是通过**kwargs属性这种方式，一种是通过操作 matplotlib.font_manager.FontProperties 方法\n",
    "#该案例中对于x_label采用**kwargs调整字体属性，y_label则采用 matplotlib.font_manager.FontProperties 方法调整字体属性\n",
    "#该链接是FontProperties方法的介绍 https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties\n",
    "x1 = np.linspace(0.0, 5.0, 100)\n",
    "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)\n",
    "\n",
    "font = FontProperties()\n",
    "font.set_family('serif')\n",
    "font.set_name('Times New Roman')\n",
    "font.set_style('italic')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 3))\n",
    "fig.subplots_adjust(bottom=0.15, left=0.2)\n",
    "ax.plot(x1, y1)\n",
    "ax.set_xlabel('time [s]', fontsize='large', fontweight='bold')\n",
    "ax.set_ylabel('Damped oscillation [V]', fontproperties=font)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### 6.字体的属性设置\n",
    " 字体设置一般有全局字体设置和自定义局部字体设置两种方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cmmi10\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "DejaVu Sans\n",
      "\n",
      "STIXSizeFourSym\n",
      "\n",
      "DejaVu Sans Display\n",
      "\n",
      "DejaVu Serif\n",
      "\n",
      "STIXGeneral\n",
      "\n",
      "STIXNonUnicode\n",
      "\n",
      "STIXSizeFourSym\n",
      "\n",
      "STIXSizeThreeSym\n",
      "\n",
      "STIXGeneral\n",
      "\n",
      "cmr10\n",
      "\n",
      "STIXNonUnicode\n",
      "\n",
      "cmsy10\n",
      "\n",
      "DejaVu Sans\n",
      "\n",
      "STIXSizeFiveSym\n",
      "\n",
      "DejaVu Sans\n",
      "\n",
      "STIXSizeThreeSym\n",
      "\n",
      "DejaVu Serif Display\n",
      "\n",
      "DejaVu Sans\n",
      "\n",
      "cmex10\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "STIXNonUnicode\n",
      "\n",
      "STIXSizeOneSym\n",
      "\n",
      "STIXSizeTwoSym\n",
      "\n",
      "DejaVu Serif\n",
      "\n",
      "STIXNonUnicode\n",
      "\n",
      "cmb10\n",
      "\n",
      "STIXGeneral\n",
      "\n",
      "STIXGeneral\n",
      "\n",
      "DejaVu Serif\n",
      "\n",
      "STIXSizeOneSym\n",
      "\n",
      "cmss10\n",
      "\n",
      "STIXSizeTwoSym\n",
      "\n",
      "cmtt10\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "DejaVu Serif\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "Elephant\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Dubai\n",
      "\n",
      "Microsoft New Tai Lue\n",
      "\n",
      "Ravie\n",
      "\n",
      "Verdana\n",
      "\n",
      "Elephant\n",
      "\n",
      "Microsoft Tai Le\n",
      "\n",
      "Book Antiqua\n",
      "\n",
      "Gill Sans MT Ext Condensed Bold\n",
      "\n",
      "Nirmala UI\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "FZShuTi\n",
      "\n",
      "Lucida Fax\n",
      "\n",
      "Eras Demi ITC\n",
      "\n",
      "STHupo\n",
      "\n",
      "Constantia\n",
      "\n",
      "Ebrima\n",
      "\n",
      "Symbol\n",
      "\n",
      "DengXian\n",
      "\n",
      "MS Reference Sans Serif\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Tahoma\n",
      "\n",
      "Arial\n",
      "\n",
      "Agency FB\n",
      "\n",
      "Corbel\n",
      "\n",
      "Javanese Text\n",
      "\n",
      "Castellar\n",
      "\n",
      "Lucida Sans\n",
      "\n",
      "FZYaoTi\n",
      "\n",
      "Lucida Sans\n",
      "\n",
      "Tahoma\n",
      "\n",
      "Lucida Sans Typewriter\n",
      "\n",
      "Gill Sans Ultra Bold Condensed\n",
      "\n",
      "STZhongsong\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Algerian\n",
      "\n",
      "Matura MT Script Capitals\n",
      "\n",
      "Franklin Gothic Demi\n",
      "\n",
      "Cooper Black\n",
      "\n",
      "Lucida Handwriting\n",
      "\n",
      "Mistral\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Lucida Bright\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Parchment\n",
      "\n",
      "Perpetua Titling MT\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Brush Script MT\n",
      "\n",
      "Freestyle Script\n",
      "\n",
      "Calibri\n",
      "\n",
      "Colonna MT\n",
      "\n",
      "Century Schoolbook\n",
      "\n",
      "Georgia\n",
      "\n",
      "Tw Cen MT\n",
      "\n",
      "Lucida Bright\n",
      "\n",
      "Gadugi\n",
      "\n",
      "Constantia\n",
      "\n",
      "STFangsong\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Gill Sans MT\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "Calisto MT\n",
      "\n",
      "Century Schoolbook\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Mongolian Baiti\n",
      "\n",
      "Lucida Sans Typewriter\n",
      "\n",
      "Berlin Sans FB\n",
      "\n",
      "Perpetua\n",
      "\n",
      "Lucida Fax\n",
      "\n",
      "Century Gothic\n",
      "\n",
      "Gill Sans MT\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Gadugi\n",
      "\n",
      "Rockwell\n",
      "\n",
      "Segoe Script\n",
      "\n",
      "FangSong\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Papyrus\n",
      "\n",
      "Californian FB\n",
      "\n",
      "Microsoft YaHei\n",
      "\n",
      "High Tower Text\n",
      "\n",
      "Leelawadee\n",
      "\n",
      "Rockwell Extra Bold\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Microsoft JhengHei\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Lucida Fax\n",
      "\n",
      "Microsoft Uighur\n",
      "\n",
      "Rage Italic\n",
      "\n",
      "Cambria\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Perpetua\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Tw Cen MT Condensed Extra Bold\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Wingdings 3\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Calisto MT\n",
      "\n",
      "Century Gothic\n",
      "\n",
      "Arial Rounded MT Bold\n",
      "\n",
      "Candara\n",
      "\n",
      "STSong\n",
      "\n",
      "Maiandra GD\n",
      "\n",
      "Microsoft Uighur\n",
      "\n",
      "Engravers MT\n",
      "\n",
      "Vladimir Script\n",
      "\n",
      "DengXian\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Calibri\n",
      "\n",
      "Gigi\n",
      "\n",
      "Book Antiqua\n",
      "\n",
      "Bernard MT Condensed\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Modern No. 20\n",
      "\n",
      "Britannic Bold\n",
      "\n",
      "Nirmala UI\n",
      "\n",
      "Haettenschweiler\n",
      "\n",
      "Book Antiqua\n",
      "\n",
      "Californian FB\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Calisto MT\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Segoe Print\n",
      "\n",
      "Franklin Gothic Medium\n",
      "\n",
      "Bookman Old Style\n",
      "\n",
      "Lucida Sans Unicode\n",
      "\n",
      "Consolas\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "STKaiti\n",
      "\n",
      "Monotype Corsiva\n",
      "\n",
      "Microsoft PhagsPa\n",
      "\n",
      "Onyx\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Vivaldi\n",
      "\n",
      "Arial\n",
      "\n",
      "Franklin Gothic Medium\n",
      "\n",
      "Bookman Old Style\n",
      "\n",
      "Bradley Hand ITC\n",
      "\n",
      "Segoe MDL2 Assets\n",
      "\n",
      "Centaur\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Microsoft Sans Serif\n",
      "\n",
      "Script MT Bold\n",
      "\n",
      "Lucida Bright\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Myanmar Text\n",
      "\n",
      "Cambria\n",
      "\n",
      "Kristen ITC\n",
      "\n",
      "Sylfaen\n",
      "\n",
      "Leelawadee\n",
      "\n",
      "Rockwell Condensed\n",
      "\n",
      "Calibri\n",
      "\n",
      "High Tower Text\n",
      "\n",
      "Cambria\n",
      "\n",
      "Wingdings\n",
      "\n",
      "Courier New\n",
      "\n",
      "Lucida Calligraphy\n",
      "\n",
      "MT Extra\n",
      "\n",
      "Microsoft PhagsPa\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Calisto MT\n",
      "\n",
      "Consolas\n",
      "\n",
      "Lucida Sans\n",
      "\n",
      "Calibri\n",
      "\n",
      "Impact\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Tempus Sans ITC\n",
      "\n",
      "Verdana\n",
      "\n",
      "Gill Sans MT\n",
      "\n",
      "French Script MT\n",
      "\n",
      "Juice ITC\n",
      "\n",
      "Dubai\n",
      "\n",
      "Niagara Solid\n",
      "\n",
      "Lucida Fax\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Franklin Gothic Heavy\n",
      "\n",
      "Goudy Old Style\n",
      "\n",
      "Bell MT\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Arial\n",
      "\n",
      "Goudy Old Style\n",
      "\n",
      "Calibri\n",
      "\n",
      "Lucida Sans Typewriter\n",
      "\n",
      "Copperplate Gothic Bold\n",
      "\n",
      "Berlin Sans FB Demi\n",
      "\n",
      "Garamond\n",
      "\n",
      "Bell MT\n",
      "\n",
      "Bookman Old Style\n",
      "\n",
      "Gloucester MT Extra Condensed\n",
      "\n",
      "Segoe UI Symbol\n",
      "\n",
      "Arial\n",
      "\n",
      "Perpetua\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Harlow Solid Italic\n",
      "\n",
      "Baskerville Old Face\n",
      "\n",
      "Georgia\n",
      "\n",
      "Leelawadee UI\n",
      "\n",
      "Goudy Stout\n",
      "\n",
      "Franklin Gothic Book\n",
      "\n",
      "HoloLens MDL2 Assets\n",
      "\n",
      "STCaiyun\n",
      "\n",
      "Webdings\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Arial\n",
      "\n",
      "Poor Richard\n",
      "\n",
      "Playbill\n",
      "\n",
      "Franklin Gothic Medium Cond\n",
      "\n",
      "STLiti\n",
      "\n",
      "Bauhaus 93\n",
      "\n",
      "Microsoft JhengHei\n",
      "\n",
      "Wide Latin\n",
      "\n",
      "Corbel\n",
      "\n",
      "Gill Sans MT\n",
      "\n",
      "Century\n",
      "\n",
      "Candara\n",
      "\n",
      "Corbel\n",
      "\n",
      "SimSun-ExtB\n",
      "\n",
      "Lucida Sans\n",
      "\n",
      "Malgun Gothic\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Corbel\n",
      "\n",
      "Chiller\n",
      "\n",
      "Dubai\n",
      "\n",
      "Candara\n",
      "\n",
      "Bell MT\n",
      "\n",
      "Magneto\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "LiSu\n",
      "\n",
      "Informal Roman\n",
      "\n",
      "Consolas\n",
      "\n",
      "Century Gothic\n",
      "\n",
      "Dubai\n",
      "\n",
      "Book Antiqua\n",
      "\n",
      "Segoe UI Emoji\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Courier New\n",
      "\n",
      "Harrington\n",
      "\n",
      "Ebrima\n",
      "\n",
      "Courier New\n",
      "\n",
      "Perpetua Titling MT\n",
      "\n",
      "Segoe UI Historic\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Agency FB\n",
      "\n",
      "Old English Text MT\n",
      "\n",
      "Lucida Bright\n",
      "\n",
      "Broadway\n",
      "\n",
      "Leelawadee UI\n",
      "\n",
      "Stencil\n",
      "\n",
      "Microsoft YaHei\n",
      "\n",
      "Segoe Print\n",
      "\n",
      "Verdana\n",
      "\n",
      "Ink Free\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "Eras Bold ITC\n",
      "\n",
      "Century Gothic\n",
      "\n",
      "Arial\n",
      "\n",
      "MS Outlook\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Lucida Sans Typewriter\n",
      "\n",
      "Curlz MT\n",
      "\n",
      "Century Schoolbook\n",
      "\n",
      "Tw Cen MT Condensed\n",
      "\n",
      "Marlett\n",
      "\n",
      "Georgia\n",
      "\n",
      "Georgia\n",
      "\n",
      "OCR A Extended\n",
      "\n",
      "Jokerman\n",
      "\n",
      "Blackadder ITC\n",
      "\n",
      "Lucida Console\n",
      "\n",
      "Tw Cen MT\n",
      "\n",
      "Tw Cen MT Condensed\n",
      "\n",
      "Bookman Old Style\n",
      "\n",
      "Showcard Gothic\n",
      "\n",
      "Kunstler Script\n",
      "\n",
      "MS Gothic\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Copperplate Gothic Light\n",
      "\n",
      "Perpetua\n",
      "\n",
      "Californian FB\n",
      "\n",
      "Candara\n",
      "\n",
      "MS Reference Specialty\n",
      "\n",
      "Leelawadee UI\n",
      "\n",
      "Bookshelf Symbol 7\n",
      "\n",
      "Franklin Gothic Demi Cond\n",
      "\n",
      "Microsoft YaHei\n",
      "\n",
      "Garamond\n",
      "\n",
      "Niagara Engraved\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "Gill Sans MT Condensed\n",
      "\n",
      "DengXian\n",
      "\n",
      "SimSun\n",
      "\n",
      "Gabriola\n",
      "\n",
      "Eras Light ITC\n",
      "\n",
      "Nirmala UI\n",
      "\n",
      "Candara\n",
      "\n",
      "KaiTi\n",
      "\n",
      "Constantia\n",
      "\n",
      "Bodoni MT\n",
      "\n",
      "YouYuan\n",
      "\n",
      "Snap ITC\n",
      "\n",
      "Viner Hand ITC\n",
      "\n",
      "MV Boli\n",
      "\n",
      "Consolas\n",
      "\n",
      "Palace Script MT\n",
      "\n",
      "Arial\n",
      "\n",
      "Century Schoolbook\n",
      "\n",
      "Berlin Sans FB\n",
      "\n",
      "Constantia\n",
      "\n",
      "Pristina\n",
      "\n",
      "Tw Cen MT\n",
      "\n",
      "Franklin Gothic Heavy\n",
      "\n",
      "Arial\n",
      "\n",
      "Microsoft Tai Le\n",
      "\n",
      "Franklin Gothic Demi\n",
      "\n",
      "Tw Cen MT\n",
      "\n",
      "Corbel\n",
      "\n",
      "Eras Medium ITC\n",
      "\n",
      "STXinwei\n",
      "\n",
      "Cambria\n",
      "\n",
      "Malgun Gothic\n",
      "\n",
      "Arial\n",
      "\n",
      "Garamond\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Malgun Gothic\n",
      "\n",
      "Footlight MT Light\n",
      "\n",
      "SimHei\n",
      "\n",
      "Calibri\n",
      "\n",
      "Forte\n",
      "\n",
      "Microsoft Yi Baiti\n",
      "\n",
      "Verdana\n",
      "\n",
      "Segoe Script\n",
      "\n",
      "Rockwell\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Microsoft New Tai Lue\n",
      "\n",
      "Candara\n",
      "\n",
      "Franklin Gothic Book\n",
      "\n",
      "Wingdings 2\n",
      "\n",
      "Bahnschrift\n",
      "\n",
      "Imprint MT Shadow\n",
      "\n",
      "Myanmar Text\n",
      "\n",
      "Rockwell\n",
      "\n",
      "STXihei\n",
      "\n",
      "Rockwell\n",
      "\n",
      "Edwardian Script ITC\n",
      "\n",
      "Courier New\n",
      "\n",
      "Gill Sans Ultra Bold\n",
      "\n",
      "Microsoft Himalaya\n",
      "\n",
      "Corbel\n",
      "\n",
      "Rockwell Condensed\n",
      "\n",
      "MingLiU-ExtB\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Microsoft JhengHei\n",
      "\n",
      "STXingkai\n",
      "\n",
      "Felix Titling\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "Goudy Old Style\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#首先可以查看matplotlib所有可用的字体\n",
    "from matplotlib import font_manager\n",
    "font_family = font_manager.fontManager.ttflist\n",
    "font_name_list = [i.name for i in font_family]\n",
    "for font in font_name_list:\n",
    "    print(f'{font}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  [为方便在图中加入合适的字体，可以尝试了解中文字体的英文名称,该链接告诉了常用中文的英文名称](https://www.cnblogs.com/chendc/p/9298832.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#该block讲述如何在matplotlib里面，修改字体默认属性，完成全局字体的更改。\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']    # 指定默认字体为新宋体。\n",
    "plt.rcParams['axes.unicode_minus'] = False      # 解决保存图像时 负号'-' 显示为方块和报错的问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb0f5e1348>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#局部字体的修改方法1\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fontmg\n",
    "\n",
    "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "plt.plot(x, label='小示例图标签')\n",
    "\n",
    "# 直接用字体的名字。\n",
    "plt.xlabel('x 轴名称参数', fontproperties='Microsoft YaHei', fontsize=16)         # 设置x轴名称，采用微软雅黑字体\n",
    "plt.ylabel('y 轴名称参数', fontproperties='Microsoft YaHei', fontsize=14)         # 设置Y轴名称\n",
    "plt.title('坐标系的标题',  fontproperties='Microsoft YaHei', fontsize=20)         # 设置坐标系标题的字体\n",
    "plt.legend(loc='lower right', prop={\"family\": 'Microsoft YaHei'}, fontsize=10)    # 小示例图的字体设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb0f62bf08>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#局部字体的修改方法2\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fontmg\n",
    "\n",
    "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "plt.plot(x, label='小示例图标签')\n",
    "#fname为你系统中的字体库路径\n",
    "my_font1 = fontmg.FontProperties(fname=r'C:\\Windows\\Fonts\\simhei.ttf')      # 读取系统中的 黑体 字体。\n",
    "my_font2 = fontmg.FontProperties(fname=r'C:\\Windows\\Fonts\\simkai.ttf')      # 读取系统中的 楷体 字体。\n",
    "# fontproperties 设置中文显示，fontsize 设置字体大小\n",
    "plt.xlabel('x 轴名称参数', fontproperties=my_font1, fontsize=16)       # 设置x轴名称\n",
    "plt.ylabel('y 轴名称参数', fontproperties=my_font1, fontsize=14)       # 设置Y轴名称\n",
    "plt.title('坐标系的标题',  fontproperties=my_font2, fontsize=20)       # 标题的字体设置\n",
    "plt.legend(loc='lower right', prop=my_font1, fontsize=10)              # 小示例图的字体设置\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.数学表达式\n",
    "在文本标签中使用数学表达式。有关MathText的概述，请参见 [写数学表达式](https://matplotlib.org/tutorials/text/mathtext.html#sphx-glr-tutorials-text-mathtext-py),但由于数学表达式的练习想必我们都在markdown语法和latex语法中多少有接触，故在此不继续展开，愿意深入学习的可以参看官方文档.下面是一个官方案例，供参考了解。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = np.sin(2*np.pi*t)\n",
    "\n",
    "plt.plot(t, s)\n",
    "plt.title(r'$\\alpha_i > \\beta_i$', fontsize=20)\n",
    "plt.text(1, -0.6, r'$\\sum_{i=0}^\\infty x_i$', fontsize=20)\n",
    "plt.text(0.6, 0.6, r'$\\mathcal{A}\\mathrm{sin}(2 \\omega t)$',\n",
    "         fontsize=20)\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('volts (mV)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QP3n7mkJYUjCn5K/du+lRrx4L77yT6Zs28fysWfx+2228MGsWEUFB/H3//exITaXdZ5+xrW5dOtaoQbC/Pz+uX8+Lc+bw3lVXUScmhqGzZ1O1VCnm33EHGVlZNHn/fe5s2ZJJf//N8sREVt17L7sOHeL8d95h5q235oljVVISTcqX56Jq1XhrwQKW33OPM2/4ZZcRExZGtstFg/fe4+n27TmanU1YYCAhAQGsTEpyDjyPTJ1KqeBglgwYAEByWhpTN25kX0YGy+6+G4D9GRle2z6/TBl61KtH97p16V63LpnZ2Vw5ejQfde9Orehoft6wgdfmzePzHj14oWNHrho9mpplyvDx0qXMvPVWwoOCaF25Mm9cfjmNy5cvcN92HDxISloanWvW5NkOHXjol1+YvmkT/Zo396r/5IwZtKlShTHXXce3q1bRb/Jk6pctW2BbPNSmTZHaLPcBeH9GBt3HjOGb3r2JrVyZfenphAcFFfh3/3DxYtbv2cPq++4jPSuL+u++S5/GjVmVlMSetDTuatmS/+vala379/Pcb78xq18/SoeEEPvxx/Ru0ACAh6ZOZcmAAZQKDmZ/RoYlhGLmk6QgIgOBOwEFVgK3qWpG4UuZM0WWy8WetDSeufhiAJpXrEhKWhpZLhdfrVjBxoceQkSoWqoUR7OzUVVEhOc7dOCKr7/m46uvpmONGmS5XLy7aBGVIyP5wNMvfyQrC5cqg+Pi+Ok//8Hfz4+qpUpROiQkT1dIRlYWmdnZRIWEsH7PHmrl+pS94+BB/jtzJisSEwHYfuAAgf7+/Llzp7OeFYmJNK1QgcRDh/h540Y2PvggIgJA+fBwyoaF8evmzbzy++/c3LQp1aOi8rTFisREpx0m//03q5OSuG7cOKedLq5eHXAnkDZVq3Ln998z7/bbnQPbuj17qBcTk2e9ufdtfkICV9Wpw0XVqjnrLR0S4lV/V2oq0zdt4t2rrgKgcfnyNCpfngA/vwLbYuPevUVqs9w+WbqU3g0bElu5MgBlQkML/bu/s3Ahv956K/5+fkQEBVEhIoIDGRmsSEzk9hYtaOJJysPnzePhNm0oFx4OQN2YGOfMMDIoiAd+/pnbW7Sgk92wsdiVeFIQkSrAQ0BDVU0XkXHAjcCoko7FnJo1ycnUjo4myHPAWLprF80qVCD+wAEqRkQQEuB+W+1MTaVCeLhzoP1yxQqiQ0OpGBEBwNb9+6lXtiy/33ab1/qzXC4SDx92ulK2HzhAZHAwpY7rX16dlETDcuWAYwf4HLdMmsR9rVvzZc+ebN63j25jxhDk78/yXNcmViYl0bthQ1YmJdGiYsU8B8DWVaqw6K67mLBmDRd9+ik//uc/Xl1YqkrCwYNOsliemMjLl1zCHS1b5mmzHQcPsmzXLgL9/Z0D3560NKKCg/Ns9/h9W5mYSNsqVZx5KxITeezCC73rJyfTtEIF/DxtvcTzNymsLYraZrn9tXs3vRs29Cor6O/uUmVPejqVPd1cGVlZ7ElLo3JkJCsTE70O8CsSE7knNtZp12W7dvGmp6tuwZ13MnXjRl6cM4efN2xg+GWX5Wkvc/r46kJzABAqIgFAGLDTR3GYU7B892627N/PkawsDh09ypDZs3mkbVvKhoWRePgwh48eJdvl4rHp03nwggsAeHnOHIL8/Jhwww0Mnj0bgHJhYaxNTmbHwYMAHMjIYNv+/QT4ud+W2w8cwKXKU7/+mu9F5tzdP1v373cOPuDueulSsyZHs7N5csYMp97yxETnwL42OZmG5cpRMSKCDXv3cjQ7G4Ckw4cBWL9nD5UjI7mvdWvqxMSQ7XJ5bX9PejoRuboyKkVEMG3TJuei6crERFSVw0eP0mvcOEZ07Uqn887j82XLANhyXMwF7duq5GSvZLR1/37Oz/UJHyAmNJT1e/aQmZ3NnrQ0Xp0711mmoLYoapvlVjEigtVJSQBku1zsTU8v8O/u7+dHSEAAOz2ju16YNYtbmjZFRLz2DyAmLIxVnvW+v3gxe9PTqR4Vxaa9ewnw8+OaevXo26QJGVlZ+baXOX1K/ExBVXeIyBvAdiAdmK6qhY+bM2eU5YmJ9G3ShIs++4z0zEye79CBtlWrAvB8hw7EeoaO3tykCXe0bMm3q1Yxe9s2fu7blwA/P0IDApi2cSNX1K7Nq1260PmLLwgNDCQ8MJAPu3cH4MXOnWn/2WdULVWKmLAwYitVyhPHysRE2ni2e0nNmvQZP56xq1ax4I47eLJdO5q8/z7NK1akSmSk16f5V7t04eCRIwQHBBASEEDj8uXpUa8ejUeOJCwwkGvq1eOJiy7i5okTOZyZSZC/Pzc1bkwrT5dJjpjQUKpHRdHwvfcY2rkzt7dowaytW2nw3nuEetb7Zc+e3DhhAvfGxtKxRg2qRUVx2VdfcVuLFtQvW5aUtDQajxzJR1df7XQPHb9vKxMTaX7ppQBs2bePalFRztlXjhaVKtGyUiUajRxJ1VKliAwKchJpQW1R1DbL7dELL+SmCRP4dvVqAvz8+KB7dy6oUiXfvzvAiK5dueyrr3CpckWtWrzSpQvgvkhfKzraWe/T7drRd+JE3l6wgLoxMU4X32fLlvHdmjVEBgdTJTKSz3r0KPB9aU4P0eOGghX7BkXKABOAPsB+4DtgvKp+fVy9AcAAgOrVq7fatm1bicb5bzS4f397yI45pwzeupXBo0b5OgyfEZElqhp7Msv4ovvoUmCLqiaraiYwEbjo+Eqq+pGqxqpqbDlP36oxxpji5YuksB1oKyJh4j4H7gKs9UEcxhhjjlPiSUFVFwLjgaW4h6P6AR+VdBzGGGPy8sn3FFR1EDDIF9s2xhhTMLv3kTHGGIclBWOMMQ5LCsYYYxyWFIwxxjgsKRhjjHFYUjDGGOOwpGCMMcZhD9kxjtLVqzP4Hzxa0pgzTWnP8yxM0VlSMI5Hhg71dQjGGB+z7iNjjDEOSwrGGGMclhSMMcY4LCkYY4xxWFIwxhjjsKRgjDHGYUnBGGOMw5KCMcYYhyUFY4wxDksKxhhjHJYUjDHGOCwpGGOMcVhSMMYY47CkYIwBID4+ni5dutCgQQMaNWrEk08+iar6OixTwiwpGGMACAgIYNiwYaxdu5Zly5axcOFCJk6c6OuwTAmzpGDOCuPGjWPUqFF5yvv3709sbGzJB3ScgQMHIiL5/owdO9bX4RVJpUqVnLYMCgqiadOmxMfH+zgqU9LsITvmrDBu3DhSUlLo37+/V/nzzz9Penq6b4LKZeXKlbRv357XX389z7zGjRv7IKJ/Zs+ePUyePJnp06f7OhRTwiwpmLNarVq1fB0C4E4KN998M23btvV1KP/YkSNH6N27N4888ggNGjTwdTimhFn30Vlq/vz5XHPNNVSuXJnw8HCaN2/O6NGjverkdK3MmDGDpk2bEh4eTvv27Vm9evUJ1z937lw6duxIWFgYMTEx3HXXXaSmpnrVGTlyJNWqVSM8PJyrr76aGTNmICLExcU5dTp16kTv3r29louLi0NEWLVqVZH2pX///kyYMIHZs2c7XTKDBw/22sfcxo0bR5MmTQgODqZatWo8++yzZGVlnba2OV5SUhJJSUk0atTopJc9FS6Xi/DwcN555x0effRRypcvT5kyZZyzlK+++oqGDRsSERFBr169vM6ksrOzeeutt2jatCkhISFUqlSJvn37OheUs7Oz6du3Ly1atOCxxx4rkf0xZxY7UzhLbdu2jXbt2nHPPfcQEhLCvHnzuO222/Dz8+Omm25y6m3fvp0nnniCZ599ltDQUB5//HFuuOEGVq1ahYjku+558+bRpUsXrr32WsaPH8+ePXt4+umn2bdvH+PHjwdgypQp3H///dxzzz1ce+21zJ49m9tvv71Y9uX5559n+/bt7N+/n5EjRwJQtWrVfNc1ffp0+vTpw6233srrr7/OihUreP7559mzZw8ffPCBV90TtU1cXBydO3dm1qxZdOrUqcD4V6xYAUC9evXyJB9/f/887ayqZGdnn7BdAgLy//fcvHkzaWlpvPXWW9x0002MGTOGUaNG8eSTT7Jt2za2bNnC66+/zpYtW3jooYf4/PPPue+++3C5XPTu3Zu4uDiefvppWrVqRXx8PL/88osT4913301kZCRvvvnmCeMz5yhVPeN/WrVqpaZgLpdLMzMzdcCAAdq5c2envF+/furv76/r1693yiZNmqSArl27tsD1tW/fXjt16uRVNnPmTAV05cqVqqraunVrvfLKK73q3HnnnQrorFmznLKOHTvqdddd51Vv1qxZXusqyr5cd9112rFjxzz1+/Xrp7nfH23atMkT+7Bhw9TPz0/j4+O9ljtR28TFxam/v7/GxcXl2W5ub775pgL5/uRuixyff/55gfVz/xRkwoQJCug777zjlG3dulUBveSSS9TlcjnlTZo00YceekhVVd944w0NDw/XNWvW5LveuXPnKqCNGzfWZs2aabNmzby2Yc4+wGI9yeOtnSmcpfbt28egQYOYMmUKO3bscD55VqlSxatejRo1qFOnjjPdsGFDABISEqhfv36e9aalpTF//nxGjBjh9am3ffv2BAYGsmTJEho0aMCyZcsYMWKE17K9evXik08+KbZ9OZHs7GyWLl3K22+/7VXep08fnnrqKebPn8/111/vlJ+obTp27Jjnk39+Vq5cSe3atfMdZdS0adM8ZVdffTV//vlnUXcr3+2VLl2ae++91yk7fPgwAE8//bTXmcnhw4eJjo7G5XIxfPhwHnjggQKvE7Rr186+l2AsKZyt+vfvz4IFC3j++edp2LAhpUqV4v3332fKlCle9UqXLu01HRQUBEBGRka+6923bx/Z2dncd9993HfffXnmx8fHk5ycTFZWFuXLl/ead/z06d6XE0lJSSEzM5MKFSp4ledM792716v8ZNumICtXrqRZs2ZFHhobHR1NVFTUSW3j+O3lJOkcK1asICAggA4dOjhlaWlpbN26lcaNG7NixQqSkpLo2bPnKW/X/DtYUjgLZWRk8NNPP/Huu+9yzz33OOUul+sfr7t06dLOhdyrrroqz/zKlStTrlw5AgICSEpK8pp3/DRASEgIR48e9SrLfXA+nftStmxZAgMD88SRmJgIuA/Gp5vL5WLNmjV069atyMt88cUX3HbbbSesV9Cn9hUrVtCnTx+vsuXLl1O/fn2Cg4OdspUrV+JyuWjatCkbN24E3N9FMKYwPkkKIlIa+ARojLv/9HZVne+LWM5GR44cITs72+sAkJqayvfff1/gxeOiCg8Pp23btqxbt44XXnihwHrNmzdnypQpXgfy/L79WrVqVebMmeNVNmPGDOd1UfclKCjohJ/g/f39adWqFd99951X18q4cePw8/PjwgsvLHT5U7FhwwbS09NPauTRP+k+Sk9PZ9OmTTRr1syrfMWKFfmWhYeHU6tWLQ4dOgTA2rVrqV69+ilt2/w7+OpM4R1gqqr2FpEgIMxHcZyVoqKiaN26NUOHDqVUqVL4+fnx2muvERUVxcGDB//x+ocPH06XLl3w8/Ojd+/eREZGsn37dn766Sdefvll6tatyzPPPEOvXr2499576dmzJ7Nnz2bq1Kl51tWzZ08+/fRTBg4cSLdu3Zg1axbTpk076X2pX78+U6ZMYfLkyVStWpXKlStTuXLlPNsbMmQIV1xxBbfddhs33ngjK1eu5Pnnn+euu+4qcMRSQWbPnk2XLl2YOXMmHTt2zLfOypUrAXdyW7Bggde86Oho6tatm2eZmJgYYmJiTiqWHKtXr8blcuWbAB566KE8ZY0aNcLPz48mTZrQqFEj7r33XoYOHUrlypVZs2YN8fHxDBs27JRiMeeok70y/U9/gFLAFkCKuoyNPsprw4YN2rlzZw0LC9Nq1arpsGHDdNCgQRoTE+PUOX5kjqrqli1bFNAffvih0PUvWLBAr7jiCo2MjNSwsDBt0KCBDhw4UPfv3+/UGTFihFapUkVDQ0O1a9euOm3atHxH3LzyyitatWpVjYiI0L59++qUKVO8Rh8VZV+Sk5P12muv1TJlyiiggwYNKnAfv/nmG23cuLEGBgZqlSpV9JlnntHMzEyvOkVpm5xRUvmNIMrxwgsvFDh6aODAgYW28an47LPPNCwsTLOzs52yvXv3KqBTp071qtuhQwe98847nenNmzdrr169tGzZshoWFqbNmjXTsWPHnvYYzZmDUxh9JFrCow1EpDnwEbAGaAYsAR5W1cMFLRMbG6uLFy8umQDNKVu1ahVNmjQ54bh+Y0zJEJElqnpSNwfzxTeaA4CWwPuq2gI4DDx9fCURGSAii0VkcXJycknHaIwx/0q+SAoJQIKqLvRMj8edJLyo6keqGquqseXKlSvRAI0x5t+qxC80q+puEYkXkXqqug7ogrsryZzlGjdubF9+MuYs56vRRw8Coz0jjzYDJx60bYwxptj5JCmo6l+A75+MYowxxovdOtsYY4zDkoIxxhiHJQVjjDGOAq8piEieYaK5qerS0x+OMcYYXyrsQnNhj15S4JLTHIsxxhgfKzApqGrnkgzEGGOM753wmoKIhInIcyLykWe6joh0L/7QjDHGlLSiXGj+HDgKXOSZTgBeKraIjDHG+ExRkkItVR0OZAKoajrwz57kYowx5oxUlKRwVERCcV9cRkRqAUeKNSpjjDE+UZTbXAwCpgLVRGQ00A7oX5xBGWOM8Y0TJgVVnSEiS4G2uLuNHlbVlGKPzBhjTIkr6g3xOgLtcXchBQKTii0iY4wxPlOUIakjgXuAlcAq4G4Rea+4AzPGGFPyinKm0BFo7HkINCLyBe4EYYwx5hxTlNFH64DquaarASuKJxxjjDG+VNgN8X7AfQ0hClgrIos8022AP0omPGOMMSWpsO6jN0osCmOMMWeEwm6IN7skAzHGGON7RRl91FZE/hSRQyJyVESyReRgSQRnjDGmZBXlQvO7wE3ABiAUuNNTZowx5hxTpC+vqepGEfFX1WzgcxGxC83GGHMOKkpSSBORIOAvERkO7ALCizcsY4wxvlCU7qNbAH/gAeAw7u8pXFecQRljjPGNotwQb5vnZTowpHjDMcYY40uFfXltJZ5nKORHVZsWS0TGGGN8prAzhZznMF8DzAX2Fn84xhhjfKmwL69tAxCRCsB3wFLgM2Bazs3xjDHGnFtOeKFZVZ8D6gCf4n7i2gYRecXzWE5jjDHnkKKMPsJzZrDb85MFlAHGe4aoGmOMOUeccPSRiDwE9ANSgE+AJ1Q1U0T8cH/L+cniDdEYY0xJKcqX18oCvXINTQVAVV0i0r2AZYwxxpyFivI9hRcKmbf29IZjjDHGl4p0TaE4iIi/iCwTkR99FYMxxhhvPksKwMOAnWkYY8wZxCdJQUSqAt1wX7g2xhhzhvDVmcLbuEctuQqqICIDRGSxiCxOTk4uscCMMebfrMSTgmfEUpKqLimsnqp+pKqxqhpbrly5EorOGGP+3XxxptAOuEZEtgLfAJeIyNc+iMMYY8xxSjwpqOp/VbWqqtYAbgR+U9WbSzoOY4wxefly9JExxpgzTJGe0VxcVDUOiPNlDMYYY46xMwVjjDEOSwrGGGMclhSMMcY4LCkYY4xxWFIwxhjjsKRgjDHGYUnBGGOMw5KCMcYYhyUFY4wxDksKxhhjHJYUjDHGOCwpGGOMcVhSMMYY47CkYIwxxmFJwRhjjMOSgjHGGIclBWOMMQ5LCsYYYxyWFIwxxjgsKRhjjHFYUjDGGOOwpGCMMcZhScEYY4zDkoIxxhiHJQVjjDEOSwrGGGMclhSMMcY4LCkYY4xxWFIwxhjjsKRgio2q8uWlX570ctmZ2fzy8C8s/2p5MUSVV1pKGuP7jD/l5dWlLPtsGd/0+IaNUzeexsgKt/nXzYzrPa7Etpfbng17+KDZB6e07KJ3F7F20lqfxm8KZknBFBsR4eZpN5/0cv6B/pRrUA7N1iIvEz8/nlciXmHF6BVFXubA9gMAhJUNo9eYXl5lJ1omt3Xfr6NU1VJc/fHVpKxLYcaTM5jx5Aw2zdhU4HpSd6UypvsYJt0yiaWfLGXWoFnMeXnOScVdo1MN0lLS8syf3G8yu//azYctPyzS+k5FTJ0YAsMCT2nZ1ve1pkHPBgXGb3zLkoIpVn7+p/YWCwgJOKn61S6sRljZMBpe17BI9dP2pBE3JM6Z9vP3Q13KtIHTClxm3+Z9LHh7QZ7ylL9T8A/yJ7x8OG0ebEPHQR3pOKgj53c5v8B1RVaKpHJsZaq1q0bLO1vSeUhnmtzU5IRx547RLyD/tu36blcqNq9I/9n9T7i+f8I/2P+UlhM/AQqO3/jWyf3nGeMRNyQOFNoObMv3d3xPtXbV2LNuD4HhgexYsINbfr2F+D/i+fO9P+kzsQ+7/9rNrmW72DJzC01vbkrtK2szb/g8wiuEs/337XR5tQvh5cJZ8M4CwsuHs2rsKhrf1BhVZeknS1GXsuXXLfT8uierxq4ibU8aFz12Ub6xbZuzjXnD51GtXTXWjl/LtV9eS0ydGP548w+CwoM4nHSY2lfWJn5ePOt/XE/G/gzi58dz4cALSVqdxF+j/sKV5WL73O10/6A705+YTuVWlfEL8GPHoh1sjdtKjU41AHc3Svwf8aTtSSOiYgSju47m4S0Ps33udn577jeuH3c9E/4zgZqX1GTb7G0FnjltnLqR2lfWJjM9k+VfLiczLZOkVUlc88k1LP5gMUERQaz/fj1dXu3ixNj05qbO8t/0+IZWd7eizlV1WPz+Yq923f3Xbha+vZDA8EDKNSrHusnruODBC1jy4RIaXt+Qix6/iM2/buZw0mHW/7CeCx68gL0b97rbZl8G9a6tx+pvV1OvRz2Wj1rOxc9eTOMbGwOw6ttVLP14KY1vakzNS2oyqsMoun/UnTpd6zDltil0eKEDG6dudOK/5rNr+Omen2g5oCU1OtbIN37jWyWeqkWkmojMEpG1IrJaRB4u6RjMP5fzDx0SFUL5xuWp0KQCASEBXPHmFURWiWT3st3U6FiD9L3pAMx/cz4tbmtBpyGdcGW72DR9E5npmTTv15yaXWoye8hs4ufHc2j3IZrc1IR6PeoBsP7H9aTuSKX0eaWJqhHFvs37aHxTY1rf17rA2KpdVI3Unam0f6o9Le5owbop6zi0+xA7/9xJyztb0vD6hlRvX52IihHU7V6Xqm2rknk4k+ja0URUjKB5/+acf6n7U35ASACVW1UGoHr76kTXjnYSAri7USq1rETdbnUpW78sUedFOXUBwsuHE1kpklpX1OI/P/8nT6xbftvC76/8zprxawCcBFiuQTn8g/xJS0lj3eR11OhUg4ufu9grxpxP2n++/ydXvXcVda6qk2+71rykJgfiD9D72950eK4DwaWCqdejHn1/6cvc1+aSmZbJoncXERodSvUO1UlcmUi1dtXISs/i5mk30/q+1mSlZ9H6vtZcP/56fn3qVyf+Ol3rcOU7V7J2/FrK1CzDxc9eTMraFFSVii0rUqZmGa/4gyODKV2zNOTqGcwdv/E9X5y/ZQGPqWoDoC1wv4gU7ZzfnLH8AvwIKR0CQGBYINlHs726B/Zt3gdAdK1o6nary65luwiODAagYrOKJK9OJn5ePBEVIpx1ACSvSaZMrTLUvrI2l79+OWXrlSUgOIDA0IL7s/0C/AguFYz4iRNLVPUoKrWqxMhGI0ndkZqnfnESfyEsJizfrrSal9Tk4mcupvPQzoB7f8s3Lk/tK2vT/f3uhMWEccGDFzCq4yg2/LQhz/Lpe9JZ8uESgku52zK/dvXz9yM0OhQRcccgEBYTRmh0KGXOL8Ph5MMcTT1K7Str0/re1rS6q5V7mZhQxE8QEQLDAwkMDaRsvbK4slyouo/qwaWCCQoPIisjC4Dm/ZuzcvRKNk7dSM3ONQHyxJ+7vY+P3/heiScFVd2lqks9r1OBtUCVko7D/DP+Qf5kpmUC7v55dRV+UTgzLZPdf+1GXcqGXzZQvlF5di7eCcDRw0ep2LIikZUjif8j3llGXUp07Wj+fO9PMtMzSfk7hX2b95F1JIvM9MyTinff5n20vq81fX/py6znZ3k2QJ64RQRVzX//JP9l8muX9L3pJ2yT3CIrRwIQXTuaBf9bgCvbxbY52ziSeoSIihHcs/weVo5eSdqeNCdGdSmhMaFc+NiF/PzAzwD5tmt+XNkuAAKCAyhVtRS7l+9mx6IduLJcbJyWdwRVzkX/7MxsoutEIyLe8z1JIiAkgLrX1GXJB0so37g8QJ74czs+fuN7Pr3SIyI1gBbAwnzmDRCRxSKyODk5ucRjM4Wr2Lwi8X/E89N9P5F9JJvt87aTvDqZA9sPsH/LfnYu3snOxTs5mHCQ1F2pXPHWFYy7bhxjrxlLhSYVqNOtDqExofzxxh9smraJi5+5mIbXNyT7aDaT+08mfn48yWuSqdu9LtG1o3m37rusnbiW6NrRrPhqBYs/WOwVz7bft7m7Wn5YR8KCBA4mHGT/tv3sXr6b5NXJHEo8xMT/TCRhYQIt7mjhXkjgr1F/sXPxTvZu3Ev6vnQqtqzI9MenExoTSmZaJpP7TebQrkMkr0kmNDqUlL9TWPfDOme76fvS2bl4J5tnbuZI6hEa9WnEmG5jWDtpLUcOHmH/tv3s3bCXTdO9RyKl7kolYUECCQsSvEbgxN4dS2Z6JiNqj2Dvxr2ERIXw070/8fekv6nXox5hMWFOjNt+30bqjlRqXVaLHQt3MHvobGp2qZmnXRMWJLB3w172bdnnbGfx+4tZ/MFi2v+3PX7+fnQb2Y2x14zlmx7fUOWCKiQsSCBxRSKHEg8BkLE/g2WfLWPh/y3k8jcuJ3VnKgfjD5KwMIGdS3aSuiOVQ7vddZv2bUqdbse6gnLHHxgWSPLqZHb8uYPtc7fniT/njMP4juRk+BLfsEgEMBt4WVUnFlY3NjZWFy9eXFgVY0wRjeo0iv5x/Ytlmeyj2fw16i8a39jYuoTOACKyRFVjT2YZn5wpiEggMAEYfaKEYIw5fQ5sP0DqzlR2LNpR5GVS/k5h/9b97Fm/p9B6qbtSefu8t/EP9reEcBYr8TMFcXdGfgHsVdVHirKMnSkYY8zJO1vOFNoBtwCXiMhfnp+rfBCHMcaY45T4l9dUdS7ucRzGGGPOMPY9c2OMMQ5LCsYYYxyWFIwxxjgsKRhjjHFYUjDGGOOwpGCMMcZhScEYY4zDkoIxxhiHJQVjjDEOSwrGGGMclhSMMcY4LCkYY4xxWFIwxhjjsKRgjDHGYUnBGGOMw5KCMcYYhyUFY4wxDksKxhhjHJYUjDHGOCwpmLPS1v1bkSHCj+t/9Gkch44eQoYIo/4aVWCdpMNJDI4bzNb9W4slhkU7FjE4bnCR6nYa1Yne43oXSxzmxN5d9C4y5Mx+RL0lBWOKWdLhJIbMHlKsSWHI7CFFqjuy20he7fJqscRhzg0Bvg7AGF/KdmWTrdkE+Qf5OpQS0bBcQ1+HcM5Lz0wnNDDU12GcMjtTMD4xZ9scOn/RmYhXIoh6LYpOozqxbNcyZ/5fu/+iy5ddCHs5jDLDytB3Yl8SDyUWus5sVzaD4wZT/a3qBL8UTKORjRizcoxXnf6T+xP7USyT/55Mo5GNCHk5hIUJCwGY8vcUYj+KJeSlECq+UZEnZzxJZnam1/IT1kyg7oi6hL4cSofPO/B3yt+FxrR1/1aavN8EgM5fdEaGiFf3wd70vdz9w91UeKMCIS+FcNGnFznxANz/0/2Ue70cSYeTvGKQIcKMTTMY9dcoHvzlQQBn3Z1GdSownuO7jwbHDabs8LIs27WMtp+0JezlMFp82ILft/1e6H4BPP3r0zR5vwkRr0RQ9X9V6TuxL7sP7XbmL0hYQMDQAD5b9plTdiDjANXeqsbNE292ylYlraLbmG5EvhpJ5KuRXP/d9V7ryczO5PHpjzt/18pvVqbntz05mn200PhOx3ssJS2FfpP7ETM8hrCXw+g0qhOLdy72qlPj7Ro8Nu0xXpz9IlX/V5VSr5UC4EjWER74+QFKv1aa6GHRDJw6MM/76VT3rThZUjAlLm5rHF2+7EKgXyBfXPsF3/b+lourX8yO1B0AJB9OptOoTqRlpjHmujGM6DqC2Vtnc9lXlxX6z/LCrBd4+feXGdBqAN/f+D3tqrWj78S+jF051qve1v1beXLGk/y3/X/5+T8/U7NMTcatHkevcb24oMoFfH/T9wzqOIiPlnzEf2f+11lu6a6l9Bnfh2YVmzHxholcU+8abvjuhkL3tVJEJUb3Gg3Ae1e9x/w75jP/jvmA+6Bx6ZeXMmPzDF6/7HUm3ziZcuHluPSrS52D4vDLhhMVHMXdP94NuLui7v3pXu5pdQ+X1bqMbnW68diFjwE46x7ZbeTJ/DlIy0yj3+R+3N3qbibcMIFg/2B6ftuTtMy0QpdLOpzEM+2f4af//MTbV77N5n2bueSLS8h2ZQPQtmpbnrjoCQZOG8j2A9sBeGjqQ7jUxYiuIwDYuHcj7T5rR0ZWBl/1/IpRPUaxOmk1V4+9GlUF4NW5rzJ65Whe7PwiM26ZwdtXvk1UcJSznfycrvfYtd9cy7SN03jjsjf4tve3uNRF5y86s3HvRq/tjVk1htnbZjOy20i+7f0t4E6anyz9hOc7PM/oXqPZdmAbb85/02u5U9m3YqeqZ/xPq1at1Jw72n7SVlt92EpdLle+85+a8ZRGvRqlBzIOOGULExYqg9ExK8aoquqWfVuUwegP635QVdU9aXs07OUwHTxrsNe6un7dVeuOqOtM95vUTxmMLtu1zClzuVxa/a3q2n9yf69lP136qYa8FKIph1NUVfX6cddrg3cbeMX90uyXlMHo58s+L3B/VyauVAajs7bM8ir/ZMknGjg0UNenrHfKMrMz9fx3ztfHpz3ulM3dNlf9hvjpl399qb2+7aXnv3O+ph5JdeaPWDhCGUyB28+t4+cd9bpvr3OmB80apAxGZ26e6ZQt27VMGYz+suGXIq1TVTUrO0sTDiQog9HZW2c75UeyjmiTkU20yxdddPLaycpg9Kf1Pznzb554s9YdUVePZB1xytanrFe/IX7647ofVVW12+hu+ujUR4sci+rpeY/9suEXZTAatyXOqXPoyCEtO7ysDvh+gFN23lvnacU3Kmp6ZrpTlnI4RUNeCtHXfn/NKct2ZWu9EfW8/lansm8nA1isJ3m8tTMFU6IOHz3MwoSF9GvWD5H8R2Es2rGIy2tdTqngUk7ZBVUuoEbpGszdPjffZVYlrSItM43rG13vVd6nUR/W71nv1f1SJbIKzSs2d6bX71nP9gPbuaHhDWS5spyfS2peQkZWBquSVjlxXVPvGq+4ezXoddJtkOPXLb/SqnIrapap6WwToON5HVm861gXRbvq7Xi07aPc9cNdTP57MqN6jCIiKOKUt3u8QL9AOtXo5EznXHdIOJhQ6HK/bPiFiz69iKjXogh4MYCqb1UF3O2ZI8g/iC97fsmcbXPoM74Pd7a4k6vqXOXM/3Xzr/Ss3xM/8XPaoGaZmtQoXcPppmlesTmjlo9i+LzhrEhc4ZxBFOR0vccW7VhEubBydKzR0akTHhRO97rdmRvv/T7sUrMLIQEhzvTKpJVkZGXQo34Pp8xP/OhRr4fXcie7byXBLjSbErUvYx+KUimyUoF1dh3aRaNyjfKUVwivwN6Mvfkvk7rLqeO1TIR7el/6PsqHl/cqy5GSlgLAVWOuIj/xB+MB2H1ot7OOHMdPn4yUtBQWJCwg8MXAPPNqlanlNX1Tk5t4Y/4bNK3QlIvPu/iUt5mfUsGl8JNjnw9zLrpnZGUUuMyfO/7kmm+uoWf9njzd/mnKh5dHENp+2jbPck0rNKVhuYYsT1zOfa3v85qXkpbCsHnDGDZvWJ5t5LT7cx2ew0/8GPnnSJ769SmqRFbhiYue4OG2D+cb2+l6j+1K3ZXnveLUSd+bpyy3nO6/E71fTnbfSoIlBVOiyoSUwU/8nIN4fipFVCIpLSlPeeLhRFpVapX/Mp4DQNLhJGLCYo4t47lwGB0a7ZQJ3p8ec+Z91P0jWlRqkWfdNUvXBKBiREWvM46c7Z2q6NBoYivH8n639/PMC/YPdl5nubIY8MMAmpRvwuqk1Xy05CMGtBpwyts9HSb9PYlyYeX4tve3zqfxbfu35Vv3nQXv8HfK39QvW58Hf3mQObfNcZJQdGg0Pev35M6Wd+ZZrmxYWQBCAkIY2nkoQzsPZcOeDXyw+AMemfYI9crW48raV+ZZ7nS9xypFVsr375t4ONHr/QTkOSOpGFERcL8/ctc9fn0nu28lwbqPTIkKDwqnTZU2fLniywJPldtUacO0jdNIPZLqlP2540+27t9K++rt812mcfnGhAWG8d2a77zKx60ZR92YupQLL1dgTPXK1qNKZBW27t9KbOXYPD85SaZ1ldZ8v+57r7gnrp14wn0u6JN3l5pd2Lh3I9WjqufZZpMKTZx6r/z+Cuv2rGPKjVN4qt1TPD79ca/vPBTlk/3plp6ZTqB/oNfBcPTK0XnqrUtZx7O/PctLl7zEd9d/x587/+St+W8587vU7MKqpFW0qtQqTxvUKF0jz/rqxNThjcvfINg/mDXJa/KN7XS9x9pUaUPS4STmbJvj1EnLTOOn9T/Rvlr+78McTco3ISQghCl/T3HKXOpiyropBS5TlH0rCXamYErca5e+xqVfXkrX0V0Z0GoA4YHhzE+YT2zlWLrX7c6jFz7K+4vf54qvr+Cpdk9x6Oghnp75NE3KN+G6htflu87o0GgeafMIL815iQC/AGIrxzJx7UR+3vAzY68bm+8yOfzEjzcvf5NbJt3CwSMH6VqnK0H+QWzet5nJf09m/A3jCQsM46l2T9HmkzbcMP4G7mhxB6uSVvHpsk9PuL/Vo6oTGhDKF8u/ICo4ikD/QGIrx3Jrs1v5YPEHdBrViccvepzzy5zPnrQ9LNqxiIoRFRl44UCW7VrGS3NeYkTXEdQsU5NBnQbx44YfuX3K7cy8dSYiQv2y9QH3J/JLal5CqeBS1Ctb7+T/MCfhslqX8fbCt3lk6iNcXfdq/oj/g69Xfu1VJ9uVTb/J/WhRqQWPXvgofuLHkE5DeG7Wc3Sr2436ZeszuNNgLvj4ArqN6cbtLW6nbFhZdhzcwYzNM+jfvD+danSi57c9aVWpFS0qtiA0MJTxa8aT5cqiw3kdCozvdLzHrqh9Be2qtaPP+D681uU1YsJieOOPN0jPSueJdk8U2j4xYTEMaDmAQXGDCPALoFH5Rny89GMOHT3kVe9U9q3YneyVaV/82Oijc0/clji9+LOLNfSlUI16NUo7jerkNSJo6c6l2nlUZ2f+TeNv0t2pu535x48+UnWPgHnhtxe06v+qauDQQG3wbgP9evnXXtvtN6mftvow//fTz+t/1vaftdewl8M08pVIbfZ+M3125rOamZ3p1Bm3apzWeqeWBr8YrO0+baeLEhadcPSRqurXy7/WOv9XRwOHBnqNPtmfvl8f+vkhJ+Yqb1bRnt/01Lnb5jojdy7/6nKvdS3btUwDhwbq/y34P1V1j556YvoTWumNSiqDRTt+3rHAOPIbfRQzLCZPPQajIxaOKHSfhs0dplX/V1XDXg7TLl900fUp672We2XOKxr2cpjX6Kqs7Cxt+0lbveDjCzQrO0tVVdcmr9Xrvr1Oy7xWRkNeCtFa79TSAd8P0PgD8aqqOnzucG31YSst9WopjXglQi/4+AKdvHZyobGp/vP3mKpq0qEkvWXiLVr6tdIa8lKIdvi8gy5KWORV57y3ztPHpj2WZ/sZmRl674/3aqlXS2np10rrAz89oG/+8abX3/9U962oOIXRR6JnwNXuE4mNjdXFixefuKIxxhiHiCxR1diTWcYn1xRE5EoRWSciG0XkaV/EYIwxJq8STwoi4g+8B3QFGgI3iYjdkMUYY84AvjhTuADYqKqbVfUo8A3Q4wTLGGOMKQG+SApVgPhc0wmeMmOMMT7miyGp+X3vPM/VbhEZAOR8Q+eIiKwq1qjOHmWBFF8HcYawtjjG2uIYa4tjTnpssi+SQgJQLdd0VWDn8ZVU9SPgIwARWXyyV9DPVdYWx1hbHGNtcYy1xTEictLDNn3RffQnUEdEaopIEHAj8L0P4jDGGHOcEj9TUNUsEXkAmAb4A5+p6uqSjsMYY0xePrnNhar+DPx8Eot8VFyxnIWsLY6xtjjG2uIYa4tjTrotzopvNBtjjCkZdpdUY4wxjjM6KdjtMNxEpJqIzBKRtSKyWkR89wSOM4SI+IvIMhH50dex+JKIlBaR8SLyt+f9caGvY/IVERno+f9YJSJjRSTkxEudO0TkMxFJyj18X0SiRWSGiGzw/C5zovWcsUnBbofhJQt4TFUbAG2B+//FbZHjYWCtr4M4A7wDTFXV+kAz/qVtIiJVgIeAWFVtjHsQy42+jarEjQKOfzLP08BMVa0DzPRMF+qMTQrY7TAcqrpLVZd6Xqfi/sf/134LXESqAt2AT3wdiy+JSCmgA/ApgKoeVdX9Pg3KtwKAUBEJAMLI5/tP5zJVnQMc/7zaHsAXntdfANeeaD1nclKw22HkQ0RqAC2AhT4OxZfeBp4EXD6Ow9fOB5KBzz1daZ+ISLivg/IFVd0BvAFsB3YBB1R1um+jOiNUUNVd4P5wCZzwoeJnclIo0u0w/k1EJAKYADyiqgd9HY8viEh3IElVl/g6ljNAANASeF9VWwCHKUL3wLnI01feA6gJVAbCReRm30Z1djqTk0KRbofxbyEigbgTwmhVPfGDgc9d7YBrRGQr7i7FS0Tk68IXOWclAAmqmnPWOB53kvg3uhTYoqrJqpoJTAQu8nFMZ4JEEakE4PmddKIFzuSkYLfD8BD309E/Bdaq6v98HY8vqep/VbWqqtbA/Z74TVX/lZ8IVXU3EC8iOTc96wL47onvvrUdaCsiYZ7/ly78Sy+6H+d7oJ/ndT9gyokW8Mk3movCbofhpR1wC7BSRP7ylD3j+Wa4+Xd7EBjt+eC0GbjNx/H4hKouFJHxwFLco/WW8S/7ZrOIjAU6AWVFJAEYBLwGjBORO3AnzutPuB77RrMxxpgcZ3L3kTHGmBJmScEYY4zDkoIxxhiHJQVjjDEOSwrGGGMclhSMKYCI1Mh9x8kC6nQ62Tu1ikiciNgzhM0ZyZKCMcYYhyUFYwARaS0iK0QkRETCRWQ1EJFrfg0R+V1Elnp+ct9CoZSITBKRNSLygYj4eZa5XETme+p/57l3lTFntDP2G83GlCRV/VNEvgdeAkKBr4FDuaokAZepaoaI1AHGAjldQBfgfubHNmAq0EtE4oDngEtV9bCIPAU8Cgwtif0x5lRZUjDmmKG477mVgfuBLblvyBgIvCsizYFsoG6ueYtUdTM4txpo71lHQ2Ce+1Y8BAHzizl+Y/4xSwrGHBONu8soEDj+UY4DgUTcTzfzw33Qz3H8vWIU963fZ6jqTcUTqjHFw64pGHPMR8DzwGhg2HHzooBdqurCfXNC/1zzLvDczdcP6APMBRYA7USkNoDn7p11MeYMZ2cKxgAiciuQpapjPM8H/wO4JFeVkcAEEbkemIX7gTY55uO+G2UTYA4wSVVdItIfGCsiwZ56zwHri3dPjPln7C6pxhhjHNZ9ZIwxxmFJwRhjjMOSgjHGGIclBWOMMQ5LCsYYYxyWFIwxxjgsKRhjjHFYUjDGGOP4fz3+iJDbNmz+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#这是对前七节学习内容的总结案例\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "fig.subplots_adjust(top=0.85)\n",
    "\n",
    "# 分别在figure和subplot上设置title\n",
    "fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')\n",
    "ax.set_title('axes title')\n",
    "\n",
    "ax.set_xlabel('xlabel')\n",
    "ax.set_ylabel('ylabel')\n",
    "\n",
    "# 设置x-axis和y-axis的范围都是[0, 10]\n",
    "ax.axis([0, 10, 0, 10])\n",
    "\n",
    "ax.text(3, 8, 'boxed italics text in data coords', style='italic',\n",
    "        bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10})\n",
    "\n",
    "ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)\n",
    "font1 = {'family': 'Times New Roman',\n",
    "        'color':  'purple',\n",
    "        'weight': 'normal',\n",
    "        'size': 10,\n",
    "        }\n",
    "ax.text(3, 2, 'unicode: Institut für Festkörperphysik',fontdict=font1)\n",
    "ax.text(0.95, 0.01, 'colored text in axes coords',\n",
    "        verticalalignment='bottom', horizontalalignment='right',\n",
    "        transform=ax.transAxes,\n",
    "        color='green', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、Tick上的文本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设置tick（刻度）和ticklabel（刻度标签）也是可视化中经常需要操作的步骤，matplotlib既提供了自动生成刻度和刻度标签的模式（默认状态），同时也提供了许多让使用者灵活设置的方式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.简单模式\n",
    "可以使用axis的`set_ticks`方法手动设置标签位置，使用axis的`set_ticklabels`方法手动设置标签格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "x1 = np.linspace(0.0, 5.0, 100)\n",
    "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用axis的set_ticks方法手动设置标签位置的例子，该案例中由于tick设置过大，所以会影响绘图美观，不建议用此方式进行设置tick\n",
    "fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)\n",
    "axs[0].plot(x1, y1)\n",
    "axs[1].plot(x1, y1)\n",
    "axs[1].xaxis.set_ticks(np.arange(0., 10.1, 2.))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用axis的set_ticklabels方法手动设置标签格式的例子\n",
    "fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)\n",
    "axs[0].plot(x1, y1)\n",
    "axs[1].plot(x1, y1)\n",
    "ticks = np.arange(0., 8.1, 2.)\n",
    "tickla = [f'{tick:1.2f}' for tick in ticks]\n",
    "axs[1].xaxis.set_ticks(ticks)\n",
    "axs[1].xaxis.set_ticklabels(tickla)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<a list of 14 Line2D ticklines objects>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#一般绘图时会自动创建刻度，而如果通过上面的例子使用set_ticks创建刻度可能会导致tick的范围与所绘制图形的范围不一致的问题。\n",
    "#所以在下面的案例中，axs[1]中set_xtick的设置要与数据范围所对应，然后再通过set_xticklabels设置刻度所对应的标签\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axs = plt.subplots(2, 1, figsize=(6, 4), tight_layout=True)\n",
    "x1 = np.linspace(0.0, 6.0, 100)\n",
    "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)\n",
    "axs[0].plot(x1, y1)\n",
    "axs[0].set_xticks([0,1,2,3,4,5,6])\n",
    "\n",
    "axs[1].plot(x1, y1)\n",
    "axs[1].set_xticks([0,1,2,3,4,5,6])#要将x轴的刻度放在数据范围中的哪些位置\n",
    "axs[1].set_xticklabels(['zero','one', 'two', 'three', 'four', 'five','six'],#设置刻度对应的标签\n",
    "                   rotation=30, fontsize='small')#rotation选项设定x刻度标签倾斜30度。\n",
    "axs[1].xaxis.set_ticks_position('bottom')#set_ticks_position()方法是用来设置刻度所在的位置，常用的参数有bottom、top、both、none\n",
    "print(axs[1].xaxis.get_ticklines())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.Tick Locators and Formatters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "除了上述的简单模式，还可以使用`Tick Locators and Formatters`完成对于刻度位置和刻度标签的设置。\n",
    "其中[Axis.set_major_locator](https://matplotlib.org/api/_as_gen/matplotlib.axis.Axis.set_major_locator.html#matplotlib.axis.Axis.set_major_locator)和[Axis.set_minor_locator](https://matplotlib.org/api/_as_gen/matplotlib.axis.Axis.set_minor_locator.html#matplotlib.axis.Axis.set_minor_locator)方法用来设置标签的位置，[Axis.set_major_formatter](https://matplotlib.org/api/_as_gen/matplotlib.axis.Axis.set_major_formatter.html#matplotlib.axis.Axis.set_major_formatter)和[Axis.set_minor_formatter](https://matplotlib.org/api/_as_gen/matplotlib.axis.Axis.set_minor_formatter.html#matplotlib.axis.Axis.set_minor_formatter)方法用来设置标签的格式。这种方式的好处是不用显式地列举出刻度值列表。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "set_major_formatter和set_minor_formatter这两个formatter格式命令可以接收字符串格式（matplotlib.ticker.StrMethodFormatter）或函数参数（matplotlib.ticker.FuncFormatter）来设置刻度值的格式 。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Tick Formatters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 接收字符串格式的例子\n",
    "fig, axs = plt.subplots(2, 2, figsize=(8, 5), tight_layout=True)\n",
    "for n, ax in enumerate(axs.flat):\n",
    "    ax.plot(x1*10., y1)\n",
    "\n",
    "formatter = matplotlib.ticker.FormatStrFormatter('%1.1f')\n",
    "axs[0, 1].xaxis.set_major_formatter(formatter)\n",
    "\n",
    "formatter = matplotlib.ticker.FormatStrFormatter('-%1.1f')\n",
    "axs[1, 0].xaxis.set_major_formatter(formatter)\n",
    "\n",
    "formatter = matplotlib.ticker.FormatStrFormatter('%1.5f')\n",
    "axs[1, 1].xaxis.set_major_formatter(formatter)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 接收函数的例子\n",
    "def formatoddticks(x, pos):\n",
    "    \"\"\"Format odd tick positions.\"\"\"\n",
    "    if x % 2:\n",
    "        return f'{x:1.2f}'\n",
    "    else:\n",
    "        return ''\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)\n",
    "ax.plot(x1, y1)\n",
    "ax.xaxis.set_major_formatter(formatoddticks)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Tick Locators "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "在普通的绘图中，我们可以直接通过上图的set_ticks进行设置刻度的位置，缺点是需要自己指定或者接受matplotlib默认给定的刻度。当需要更改刻度的位置时，matplotlib给了常用的几种locator的类型。如果要绘制更复杂的图，可以先设置locator的类型，然后通过axs.xaxis.set_major_locator(locator)绘制即可  \n",
    "locator=plt.MaxNLocator(nbins=7)  \n",
    "locator=plt.FixedLocator(locs=[0,0.5,1.5,2.5,3.5,4.5,5.5,6])#直接指定刻度所在的位置  \n",
    "locator=plt.AutoLocator()#自动分配刻度值的位置  \n",
    "locator=plt.IndexLocator(offset=0.5, base=1)#面元间距是1，从0.5开始  \n",
    "locator=plt.MultipleLocator(1.5)#将刻度的标签设置为1.5的倍数  \n",
    "locator=plt.LinearLocator(numticks=5)#线性划分5等分，4个刻度  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 接收各种locator的例子\n",
    "fig, axs = plt.subplots(2, 2, figsize=(8, 5), tight_layout=True)\n",
    "for n, ax in enumerate(axs.flat):\n",
    "    ax.plot(x1*10., y1)\n",
    "\n",
    "locator = matplotlib.ticker.AutoLocator()\n",
    "axs[0, 0].xaxis.set_major_locator(locator)\n",
    "\n",
    "locator = matplotlib.ticker.MaxNLocator(nbins=10)\n",
    "axs[0, 1].xaxis.set_major_locator(locator)\n",
    "\n",
    "\n",
    "locator = matplotlib.ticker.MultipleLocator(5)\n",
    "axs[1, 0].xaxis.set_major_locator(locator)\n",
    "\n",
    "\n",
    "locator = matplotlib.ticker.FixedLocator([0,7,14,21,28])\n",
    "axs[1, 1].xaxis.set_major_locator(locator)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 此外`matplotlib.dates` 模块还提供了特殊的设置日期型刻度格式和位置的方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "import datetime\n",
    "# 特殊的日期型locator和formatter\n",
    "locator = mdates.DayLocator(bymonthday=[1,15,25])\n",
    "formatter = mdates.DateFormatter('%b %d')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "base = datetime.datetime(2017, 1, 1, 0, 0, 1)\n",
    "time = [base + datetime.timedelta(days=x) for x in range(len(x1))]\n",
    "ax.plot(time, y1)\n",
    "ax.tick_params(axis='x', rotation=70)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**其他进阶案例**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#这个案例中展示了如何进行坐标轴的移动，如何更改刻度值的样式\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.linspace(-3,3,50)\n",
    "y1 = 2*x+1\n",
    "y2 = x**2\n",
    "plt.figure()\n",
    "plt.plot(x,y2)\n",
    "plt.plot(x,y1,color='red',linewidth=1.0,linestyle = '--')\n",
    "plt.xlim((-3,5))\n",
    "plt.ylim((-3,5))\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "new_ticks1 = np.linspace(-3,5,5)\n",
    "plt.xticks(new_ticks1)\n",
    "plt.yticks([-2,0,2,5],[r'$one\\ shu$',r'$\\alpha$',r'$three$',r'four'])\n",
    "'''\n",
    "上一行代码是将y轴上的小标改成文字,其中，空格需要增加\\，即'\\ ',$可将格式更改成数字模式，如果需要输入数学形式的α，则需要用\\转换，即\\alpha\n",
    "如果使用面向对象的命令进行画图，那么下面两行代码可以实现与 plt.yticks([-2,0,2,5],[r'$one\\ shu$',r'$\\alpha$',r'$three$',r'four']) 同样的功能\n",
    "axs.set_yticks([-2,0,2,5])\n",
    "axs.set_yticklabels([r'$one\\ shu$',r'$\\alpha$',r'$three$',r'four'])\n",
    "'''\n",
    "ax = plt.gca()#gca = 'get current axes' 获取现在的轴\n",
    "'''\n",
    "ax = plt.gca()是获取当前的axes，其中gca代表的是get current axes。\n",
    "fig=plt.gcf是获取当前的figure，其中gcf代表的是get current figure。\n",
    "\n",
    "许多函数都是对当前的Figure或Axes对象进行处理，\n",
    "例如plt.plot()实际上会通过plt.gca()获得当前的Axes对象ax，然后再调用ax.plot()方法实现真正的绘图。\n",
    "\n",
    "而在本例中则可以通过ax.spines方法获得当前顶部和右边的轴并将其颜色设置为不可见\n",
    "然后将左边轴和底部的轴所在的位置重新设置\n",
    "最后再通过set_ticks_position方法设置ticks在x轴或y轴的位置，本示例中因所设置的bottom和left是ticks在x轴或y轴的默认值，所以这两行的代码也可以不写\n",
    "'''\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['left'].set_position(('data',0))\n",
    "ax.spines['bottom'].set_position(('data',0))#axes 百分比\n",
    "ax.xaxis.set_ticks_position('bottom')   #设置ticks在x轴的位置\n",
    "ax.yaxis.set_ticks_position('left')     #设置ticks在y轴的位置\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、[legend](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend)（图例）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图例的设置会使用一些常见术语，为了清楚起见，这些术语在此处进行说明：\n",
    "##### legend entry（图例条目）\n",
    "图例有一个或多个legend entries组成。一个entry由一个key和一个label组成。\n",
    "##### legend key（图例键）\n",
    "每个 legend label左面的colored/patterned marker（彩色/图案标记）\n",
    "##### legend label（图例标签）\n",
    "描述由key来表示的handle的文本\n",
    "##### legend handle（图例句柄）\n",
    "用于在图例中生成适当图例条目的原始对象\n",
    "\n",
    "以下面这个图为例，右侧的方框中的共有两个legend entry；两个legend key，分别是一个蓝色和一个黄色的legend key；两个legend label，一个名为‘Line up’和一个名为‘Line Down’的legend label"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "常用的几个参数：\n",
    "\n",
    "(1)设置图列位置\n",
    "\n",
    "plt.legend(loc='upper center') 等同于plt.legend(loc=9)\n",
    "\n",
    "\n",
    "\n",
    "| |  |  |\n",
    "| ------------------------------------------------------- | ------------------------------------------ | ------------------------------------------------------------ |\n",
    "|  0: ‘best'  \n",
    "1: ‘upper right'  \n",
    "2: ‘upper left'  \n",
    "3: ‘lower left'                                                        |   \n",
    "4: ‘lower right'  \n",
    "5: ‘right'  \n",
    "6: ‘center left'                                           |    \n",
    "7: ‘center right'  \n",
    "8: ‘lower center'  \n",
    "9: ‘upper center'  \n",
    "10: ‘center'                                                           |  \n",
    "\n",
    "\n",
    "(2)设置图例字体大小\n",
    "\n",
    "fontsize : int or float or {‘xx-small’, ‘x-small’, ‘small’, ‘medium’, ‘large’, ‘x-large’, ‘xx-large’}\n",
    "\n",
    "(3)设置图例边框及背景\n",
    "\n",
    "plt.legend(loc='best',frameon=False) #去掉图例边框  \n",
    "plt.legend(loc='best',edgecolor='blue') #设置图例边框颜色  \n",
    "plt.legend(loc='best',facecolor='blue') #设置图例背景颜色,若无边框,参数无效\n",
    "\n",
    "(4)设置图例标题\n",
    "\n",
    "legend = plt.legend([\"CH\", \"US\"], title='China VS Us')\n",
    "\n",
    "(5)设置图例名字及对应关系\n",
    "\n",
    "legend = plt.legend([p1, p2], [\"CH\", \"US\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb10938b08>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line_up, = plt.plot([1, 2, 3], label='Line 2')\n",
    "line_down, = plt.plot([3, 2, 1], label='Line 1')\n",
    "plt.legend([line_up, line_down], ['Line Up', 'Line Down'],loc=5, title='line',frameon=False)#loc参数设置图例所在的位置，title设置图例的标题，frameon参数将图例边框给去掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1eb0f042148>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#这个案例是显示多图例legend\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.random.uniform(-1, 1, 4)\n",
    "y = np.random.uniform(-1, 1, 4)\n",
    "p1, = plt.plot([1,2,3])\n",
    "p2, = plt.plot([3,2,1])\n",
    "l1 = plt.legend([p2, p1], [\"line 2\", \"line 1\"], loc='upper left')\n",
    " \n",
    "p3 = plt.scatter(x[0:2], y[0:2], marker = 'D', color='r')\n",
    "p4 = plt.scatter(x[2:], y[2:], marker = 'D', color='g')\n",
    "# 下面这行代码由于添加了新的legend，所以会将l1从legend中给移除\n",
    "plt.legend([p3, p4], ['label', 'label1'], loc='lower right', scatterpoints=1)\n",
    "# 为了保留之前的l1这个legend，所以必须要通过plt.gca()获得当前的axes，然后将l1作为单独的artist\n",
    "plt.gca().add_artist(l1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业\n",
    "1.尝试在一张图中运用所讲过的功能，对title、text、xlable、ylabel、数学表达式、tick and ticklabel、legend进行详细的设计.  \n",
    "2.阅读你可能用到文献或者相关书籍，思考自己如何才能通过学过的例子将自己认为比较好看的图给复现出来."
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